Model information processing method and device, storage medium and electronic device

Abstract

The invention discloses a model information processing method and device, a storage medium and an electronic device. The method comprises the following steps: determining at least one particle model emitted from a target model; an attribute data set of the particle model is determined, and the attribute data set at least comprises first initial attribute data and second initial attribute data which are associated with each other; in the process of controlling the particle model to move according to the attribute data set, adjusting the first initial attribute data to obtain first target attribute data, and adjusting the second initial attribute data based on the first target attribute data to obtain second target attribute data; and performing dynamic rendering display on the particle model by using the first target attribute data and the second target attribute data to obtain a representation result of the particle model emitted from the target model. The technical problem that the model rendering effect is poor is solved.

Description

Technical Field

[0001] The present disclosure relates to the technical field of computer graphics, and in particular to a model information processing method, device, storage medium and electronic device. Background Art

[0002] At present, when using 3D graphics software (Blender) for computer graphics (CG) creation, it is usually necessary to process the particle model. However, the function and controllability of the particle system provided by Blender are relatively simple. It only supports the setting of basic attribute data of the particle model, and cannot realize the linkage control between the basic attribute data. In addition, the basic attribute data related to the material of the particle model is relatively fixed, and it is impossible to add new attribute data according to the actual situation. In addition, there is a lack of more detailed and accurate control of the attribute data, which leads to the technical problem of poor model rendering effect.

[0003] Currently, no effective solution has been proposed for the above-mentioned problem of poor model rendering effect. Summary of the invention

[0004] At least some embodiments of the present disclosure provide a method, device, storage medium and electronic device for processing information of a model, so as to at least solve the technical problem of poor rendering effect of the model.

[0005] According to one embodiment of the present disclosure, a method for information processing of a model is provided. The method may include: determining at least one particle model emitted from a target model; determining an attribute data set of the particle model, wherein the attribute data set includes at least first initial attribute data and second initial attribute data that are mutually related; in the process of controlling the movement of the particle model according to the attribute data set, adjusting the first initial attribute data to obtain first target attribute data, and adjusting the second initial attribute data based on the first target attribute data to obtain second target attribute data; using the first target attribute data and the second target attribute data, dynamically rendering and displaying the particle model to obtain the performance result of the particle model emitted from the target model.

[0006] According to one embodiment of the present disclosure, a model information processing device is also provided. The device may include: a first determination unit, used to determine at least one particle model emitted from a target model; a second determination unit, used to determine an attribute data set of the particle model, wherein the attribute data set includes at least first initial attribute data and second initial attribute data that are mutually related; an adjustment unit, used to adjust the first initial attribute data in the process of controlling the movement of the particle model according to the attribute data set to obtain first target attribute data, and to adjust the second initial attribute data based on the first target attribute data to obtain second target attribute data; a rendering unit, used to dynamically render and display the particle model using the first target attribute data and the second target attribute data to obtain the performance result of the particle model emitted from the target model.

[0007] According to one embodiment of the present disclosure, a computer-readable storage medium is further provided, wherein a computer program is stored in the computer-readable storage medium, wherein the computer program is configured to execute any of the above-mentioned information processing methods of the model when running.

[0008] According to one embodiment of the present disclosure, an electronic device is also provided. The electronic device may include a memory and a processor, wherein a computer program is stored in the memory, and the processor is configured to run the computer program to execute any of the above-mentioned information processing methods of the model.

[0009] According to one embodiment of the present disclosure, a computer program product is also provided. The computer program product may include a computer program, wherein the computer program is configured to execute any of the above-mentioned information processing methods of the model when running.

[0010] According to one embodiment of the present disclosure, a computer program product is also provided. The computer program product may include a non-volatile computer-readable storage medium, wherein the non-volatile computer-readable storage medium stores a computer program, and the computer program is configured to execute the information processing method of any of the above models when running.

[0011] According to one embodiment of the present disclosure, a computer program is also provided, which is configured to execute any of the above-mentioned information processing methods of the model when running.

[0012] In at least some embodiments of the present disclosure, at least one particle model emitted from a target model is determined; an attribute data set of the particle model is determined, wherein the attribute data set includes at least first initial attribute data and second initial attribute data that are mutually related; in the process of controlling the movement of the particle model according to the attribute data set, the first initial attribute data is adjusted to obtain first target attribute data, and the second initial attribute data is adjusted based on the first target attribute data to obtain second target attribute data; the particle model is dynamically rendered and displayed using the first target attribute data and the second target attribute data to obtain the performance result of the particle model emitted from the target model. That is, in the process of controlling the movement of the particle model according to the attribute data set, the first initial attribute data is adjusted to obtain first target attribute data, and the second initial attribute data is adjusted based on the first target attribute data to obtain second target attribute data, that is, linkage control between the first target attribute data and the second target attribute data is realized, and the particle model is dynamically rendered and displayed using the first target attribute data and the second target attribute data to obtain the performance result of the particle model emitted from the target model, thereby improving the effect of model rendering and solving the technical problem of poor model rendering effect. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] The drawings described herein are used to provide a further understanding of the present disclosure and constitute a part of the present disclosure. The illustrative embodiments of the present disclosure and their descriptions are used to explain the present disclosure and do not constitute an improper limitation on the present disclosure. In the drawings:

[0014] Figure 1 is a hardware structure block diagram of a mobile terminal according to an information processing method of a model in an embodiment of the present disclosure;

[0015] Figure 2 is a flow chart of an information processing method of a model according to one embodiment of the present disclosure;

[0016] Figure 3 It is a schematic diagram of using the particle system provided by the software to achieve particle special effects according to one embodiment of the present disclosure;

[0017] Figure 4 is a schematic diagram of a controllable parameter adjustment panel of a particle system according to one embodiment of the present disclosure;

[0018] Figure 5 is a schematic diagram of an inherited emitter map material according to one embodiment of the present disclosure;

[0019] Figure 6 is a schematic diagram of the flow direction of a guided particle model according to one embodiment of the present disclosure;

[0020] Figure 7is a schematic diagram of a geometric node of Blender according to one embodiment of the present disclosure;

[0021] Figure 8 is a schematic diagram of a simulation node area according to one embodiment of the present disclosure;

[0022] Fig. 9 is a schematic diagram of a basic particle system according to one embodiment of the present disclosure;

[0023] Fig.10 is a schematic diagram of simulating node area merging point data according to one embodiment of the present disclosure;

[0024] Fig.11 is a schematic diagram of setting a curve vector field and setting a noise vector field according to one embodiment of the present disclosure;

[0025] Fig.12 is a schematic diagram of the composition structure of a particle system according to one embodiment of the present disclosure;

[0026] Fig.13 is a schematic diagram of a transmitter module according to one embodiment of the present disclosure;

[0027] Fig.14 is a schematic diagram of the internal structure of a transmitter node according to one embodiment of the present disclosure;

[0028] Fig.15 is a schematic diagram of setting the initial velocity direction of a particle model according to one embodiment of the present disclosure;

[0029] Fig.16 is a schematic diagram of an emission particle model according to one embodiment of the present disclosure;

[0030] Fig.17 is a schematic diagram of setting an independent ID according to one embodiment of the present disclosure;

[0031] Fig.18 is a schematic diagram of setting random values ​​of a particle model according to one embodiment of the present disclosure;

[0032] Fig.19 is a schematic diagram of controlling the random transparency of a particle model based on a random value of the particle model according to one embodiment of the present disclosure;

[0033] Fig. 20 is a schematic diagram of setting the life span of a particle model according to one embodiment of the present disclosure;

[0034] Fig.21 is a schematic diagram of controlling a random value of life length according to one embodiment of the present disclosure;

[0035] Fig. 22 is a schematic diagram of a random distribution control module according to one embodiment of the present disclosure;

[0036] Fig.23 is a schematic diagram of a random distribution control curve according to one embodiment of the present disclosure;

[0037] Fig.24 is a schematic diagram of a core module of a particle system according to one embodiment of the present disclosure;

[0038] Fig.25 is a schematic diagram of assigning velocity to a particle model according to one embodiment of the present disclosure;

[0039] Fig.26 is a schematic diagram of a control killing particle model according to one embodiment of the present disclosure;

[0040] Fig. 27 is a schematic diagram of controlling the velocity of a particle model according to one embodiment of the present disclosure;

[0041] Fig.28 is a schematic diagram of a random speed magnitude control module according to one embodiment of the present disclosure;

[0042] Fig.29 is a schematic diagram of a speed-with-life-change functional module according to one embodiment of the present disclosure;

[0043] Fig.30 is a schematic diagram of the change of particle model speed with life according to one embodiment of the present disclosure;

[0044] Fig.31 is a schematic diagram of a vector field control module according to one embodiment of the present disclosure;

[0045] Fig.32 is a schematic diagram of a curve vector field module according to one embodiment of the present disclosure;

[0046] Fig.33 is a schematic diagram of the influence range of a curved vector field according to one embodiment of the present disclosure;

[0047] Fig.34 is a schematic diagram of an influence range of another curved vector field according to one embodiment of the present disclosure;

[0048] Fig.35 is a schematic diagram of a noise vector field module according to one embodiment of the present disclosure;

[0049] Fig.36 is a schematic diagram of controlling random disorder of particle motion according to one embodiment of the present disclosure;

[0050] Fig.37is a schematic diagram of continuous noise control according to one embodiment of the present disclosure;

[0051] Fig.38 is a schematic diagram of low-continuity noise control according to one embodiment of the present disclosure;

[0052] Fig.39 is a schematic diagram of a particle transformation attribute control module according to one embodiment of the present disclosure;

[0053] Fig.40 is a schematic diagram of the size of a particle model changing with speed according to one embodiment of the present disclosure;

[0054] Fig.41 is a schematic diagram of the effect of the size of a particle model changing with speed according to one embodiment of the present disclosure;

[0055] Fig.42 It is a schematic diagram of the material of the particle model inheriting the emitter map material according to one embodiment of the present disclosure;

[0056] Fig.43 is a schematic diagram of a particle model guided by curve control according to one embodiment of the present disclosure;

[0057] Fig.44 is a schematic diagram of a final rendering effect according to one embodiment of the present disclosure;

[0058] Fig.45 is a structural block diagram of an information processing device according to a model of one embodiment of the present disclosure;

[0059] Fig.46 is a schematic diagram of an electronic device according to an embodiment of the present disclosure. DETAILED DESCRIPTION

[0060] In order to enable those skilled in the art to better understand the scheme of the present disclosure, the technical scheme in the embodiments of the present disclosure will be clearly and completely described below in conjunction with the drawings in the embodiments of the present disclosure. Obviously, the described embodiments are only part of the embodiments of the present disclosure, not all of the embodiments. Based on the embodiments in the present disclosure, all other embodiments obtained by ordinary technicians in the field without creative work should fall within the scope of protection of the present disclosure.

[0061] It should be noted that the terms "first", "second", etc. in the specification and claims of the present disclosure and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present disclosure described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product, or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products, or devices.

[0062] In a possible implementation, when the particle model needs to be processed, the particle system provided by Blender can usually be used. However, after practice and careful research, the inventors found that if the above method is used to process the particle model, since the method only supports the setting of the basic attribute data of the particle model, the linkage control between the basic attribute data cannot be realized, and the basic attribute data related to the material of the particle model is relatively fixed, and new attribute data cannot be added according to the actual situation, and there is a lack of more detailed and accurate control of the attribute data, resulting in a technical problem of poor model rendering effect.

[0063] Based on the above, an embodiment of the present disclosure proposes a model information processing method. In the process of controlling the movement of a particle model according to an attribute data set, the method adjusts the first initial attribute data to obtain the first target attribute data, and adjusts the second initial attribute data based on the first target attribute data to obtain the second target attribute data, that is, realizes the linkage control between the first target attribute data and the second target attribute data, and further uses the first target attribute data and the second target attribute data to dynamically render and display the particle model, and obtains the performance result of the particle model emitted from the target model, thereby achieving the improvement of the model rendering effect.

[0064] Considering that in the related art, when the particle model is processed by using Blender's own particle system, there is a technical problem of poor model rendering effect. However, in the embodiment of the present disclosure, the basic attribute data are linked and controlled to make the material of the particle model adapt to the real-time renderer, and new attribute data can be freely created according to different projects. The emitter map material can be directly and accurately inherited. At the same time, vector fields are generated using curves (such as splines) and noise attributes to accurately guide the flow direction of the particle model, thereby improving the model rendering effect and solving the technical problem of poor model rendering effect.

[0065] According to one embodiment of the present disclosure, an embodiment of a method for processing information of a model is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0066] The method embodiment can be executed in a mobile terminal, a computer terminal or a similar computing device. Taking running on a mobile terminal as an example, the mobile terminal can be a smart phone (such as an Android phone, an iOS phone, etc.), a tablet computer, a PDA, a mobile Internet device (MID for short), a tablet computer (PAD), a game console and other terminal devices. Figure 1 FIG. 1 is a hardware structure block diagram of a mobile terminal according to an information processing method of a model in an embodiment of the present disclosure. Figure 1 As shown, the mobile terminal may include one or more ( Figure 1 Only one is shown in the figure) processor 102 (the processor 102 may include but is not limited to a central processing unit (CPU), a graphics processing unit (GPU), a digital signal processing (DSP) chip, a microcontroller unit (MCU), a programmable logic device (Field Programmable Gate Array, FPGA), a neural network processor (NPU), a tensor processor (TPU), an artificial intelligence (AI) type processor, etc.) and a memory 104 for storing data. Optionally, the mobile terminal may further include a transmission device 106, an input and output device 108, and a display device 110 for communication functions. It can be understood by those skilled in the art that Figure 1 The structure shown is only for illustration and does not limit the structure of the mobile terminal. Figure 1 More or fewer components as shown, or with Figure 1 Different configurations shown.

[0067] The memory 104 can be used to store computer programs, for example, software programs and modules of application software, such as the computer program corresponding to the information processing method of the model in the embodiment of the present disclosure. The processor 102 executes various functional applications and data processing by running the computer program stored in the memory 104, that is, the information processing method of the model described above is realized. The memory 104 may include a high-speed random access memory, and may also include a non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some instances, the memory 104 may further include a memory remotely arranged relative to the processor 102, and these remote memories may be connected to the mobile terminal via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0068] The transmission device 106 is used to receive or send data via a network. The specific example of the above network may include a wireless network provided by a communication provider of the mobile terminal. In one example, the transmission device 106 includes a network adapter (Network Interface Controller, referred to as NIC), which can be connected to other network devices through a base station so as to communicate with the Internet. In one example, the transmission device 106 can be a radio frequency (RF) module, which is used to communicate with the Internet wirelessly.

[0069] The inputs in the input / output device 108 may come from a plurality of human interface devices (HIDs), such as keyboards and mice, game controllers, and other dedicated game controllers (such as steering wheels, fishing rods, dance mats, remote controls, etc.). In addition to providing input functions, some human interface devices may also provide output functions, such as force feedback and vibration of game controllers, audio output of controllers, etc.

[0070] The display device 110 may be, for example, a Heads Up Display (HUD), a touch screen liquid crystal display (LCD), and a touch display (also referred to as a "touch screen" or "touch display screen"). The LCD display enables the user to interact with the user interface of the mobile terminal. In some embodiments, the mobile terminal has a graphical user interface (GUI), and the user can interact with the GUI by finger contacts and / or gestures on the touch-sensitive surface. The human-computer interaction functions here may optionally include the following interactions: creating web pages, drawing, word processing, making electronic documents, games, video conferencing, instant messaging, sending and receiving emails, call interfaces, playing digital videos, playing digital music and / or web browsing, etc. The executable instructions for executing the above human-computer interaction functions are configured / stored in a computer program product or readable storage medium executable by one or more processors.

[0071] Those skilled in the art will understand that Figure 1 The structure shown is only for illustration and does not limit the structure of the mobile terminal. Figure 1 More or fewer components as shown, or with Figure 1 Different configurations shown.

[0072] In a possible implementation, the present disclosure provides an information processing method for a model. Figure 2 is a flow chart of a model information processing method according to one embodiment of the present disclosure, such as Figure 2 As shown, the method may include the following steps:

[0073] Step S202, determining at least one particle model emitted from the target model.

[0074] In the technical solution provided in the above step S202 of the present disclosure, at least one particle model emitted from the target model can be determined. The target model can be used to emit a particle model, and the target model can be called an emitter grid model, an emitter grid model, or a spherical grid model.

[0075] Step S204: determining an attribute data set of the particle model, wherein the attribute data set at least includes first initial attribute data and second initial attribute data that are related to each other.

[0076] In the technical solution provided in the above step S204 of the present disclosure, after determining at least one particle model emitted from the target model, the attribute data set of the particle model can be determined. Among them, the attribute data set can be a data set composed of basic attribute data of the particle model, for example, the attribute data set can at least include the speed (initial speed) of the particle model, unique identification information (ID, which can be called independent ID), random value and life span, etc., which are only used as examples here, and the content contained in the attribute data set is not specifically limited. The attribute data set can at least include first initial attribute data and second initial attribute data that are related to each other, that is, the first initial attribute data and the second initial attribute data can be changed in linkage, for example, the first initial attribute data can be life span, and the second initial attribute data can be speed, then the speed of the particle model can change with the life span of the particle model, which is only used as an example here, and the first initial attribute data and the second initial attribute data are not specifically limited.

[0077] In this embodiment, after determining at least one particle model emitted from the target model, an attribute data set containing basic attribute data such as speed, independent ID, random value and life span of the particle model can be determined, and the attribute data set can be used to enhance control over the particle model.

[0078] Step S206, in the process of controlling the movement of the particle model according to the attribute data set, adjusting the first initial attribute data to obtain first target attribute data, and adjusting the second initial attribute data based on the first target attribute data to obtain second target attribute data.

[0079] In the technical solution provided in the above step S206 of the present disclosure, after determining the attribute data set of the particle model, in the process of controlling the movement of the particle model according to the attribute data set, the first initial attribute data can be adjusted to obtain the first target attribute data, and based on the obtained first target attribute data, the second initial attribute data can be adjusted to obtain the second target attribute data. The first target attribute data can be the attribute data obtained after adjusting the first initial attribute data of the particle model. The second target attribute data can be the attribute data obtained after adjusting the first target attribute data of the particle model.

[0080] For example, the first initial attribute data is life span, and the second initial attribute data is speed. In the process of controlling the movement of the particle model according to the determined attribute data set, the life span of the particle model is adjusted to obtain the first target attribute data, and based on the obtained first target attribute data, the speed of the particle model is adjusted to obtain the second target attribute data, so as to achieve the purpose of changing the speed of the particle model with the life span of the particle model.

[0081] In this embodiment, after adjusting the first initial attribute data of the particle model to obtain the first target attribute data, the second initial attribute data is adjusted based on the first target attribute data to obtain the second target attribute data, thereby achieving the purpose of linkage control between the first target attribute data and the second target attribute data.

[0082] Step S208, using the first target attribute data and the second target attribute data, dynamically render and display the particle model to obtain a performance result of the particle model emitted from the target model.

[0083] In the technical solution provided in the above step S208 of the present disclosure, after adjusting the first initial attribute data to obtain the first target attribute data, and adjusting the second initial attribute data based on the first target attribute data to obtain the second target attribute data, the particle model can be dynamically rendered and displayed using the obtained first target attribute data and the second target attribute data to obtain the performance result of the particle model emitted from the target model. The performance result can be the flow performance result of the emitted particle model during the movement process.

[0084] In this embodiment, after obtaining the first target attribute data and the second target attribute data of the particle model, the material associated with the first target attribute data and the second target attribute data of the particle model can be determined. Further based on the material associated with the first target attribute data and the second target attribute data of the particle model, the particle model is dynamically rendered and displayed, and the performance result of the particle model emitted from the target model can be obtained.

[0085] Through the above steps S202 to S208, at least one particle model emitted from the target model is determined; the attribute data set of the particle model is determined, wherein the attribute data set includes at least first initial attribute data and second initial attribute data that are mutually related; in the process of controlling the movement of the particle model according to the attribute data set, the first initial attribute data is adjusted to obtain first target attribute data, and the second initial attribute data is adjusted based on the first target attribute data to obtain second target attribute data; the particle model is dynamically rendered and displayed using the first target attribute data and the second target attribute data to obtain the performance result of the particle model emitted from the target model. That is to say, in the process of controlling the movement of the particle model according to the attribute data set, the embodiment of the present disclosure adjusts the first initial attribute data to obtain first target attribute data, and the second initial attribute data is adjusted based on the first target attribute data to obtain second target attribute data, that is, the linkage control between the first target attribute data and the second target attribute data is realized, and the first target attribute data and the second target attribute data are further used to dynamically render and display the particle model to obtain the performance result of the particle model emitted from the target model, thereby achieving the improvement of the model rendering effect and solving the technical problem of poor model rendering effect.

[0086] The above method of the embodiment of the present disclosure is further introduced below.

[0087] As an optional embodiment, step S204, determining the attribute data set of the particle model, includes: determining initial attribute data associated with the movement process of the particle model, wherein the initial attribute data includes first initial attribute data and second initial attribute data; and determining the attribute data set based on the initial attribute data.

[0088] In this embodiment, after determining at least one particle model emitted from the target model, initial attribute data associated with the motion process of the particle model may be determined. Further based on the determined initial attribute data associated with the motion process of the particle model, an attribute data set may be determined. The initial attribute data may include first initial attribute data and second initial attribute data, and the initial attribute data may be used to enhance control of the particle model.

[0089] Optionally, after determining at least one particle model emitted from the target model, initial attribute data such as speed, independent ID, random value and life span associated with the movement process of the particle model can be determined. Further, a correlation relationship between various different initial attribute data of the particle model can be provided, for example, a correlation relationship between the speed of the particle model and the life span of the particle model can be provided, that is, the speed of the particle model changes with the life span of the particle model. Since the attribute data set of the particle model can include at least the first initial attribute data and the second initial attribute data that are interrelated, the attribute data set of the particle model can be determined based on the determined initial attribute data of the particle model.

[0090] As an optional embodiment, determining initial attribute data associated with the movement process of the particle model includes: determining control parameters of the particle model, wherein the control parameters are used to control the target model to emit the particle model; and determining the initial attribute data of the particle model based on the control parameters.

[0091] In this embodiment, after determining at least one particle model emitted from the target model, a control parameter of the particle model may be determined. Further based on the determined control parameter of the particle model, initial attribute data of the particle model may be determined. The control parameter may be used to control the target model to emit the particle model. For example, the control parameter may include at least the emission type of the target model, the density of the emitted particle model, and the initial emission direction of the particle model.

[0092] Optionally, after determining at least one particle model emitted from the target model, control parameters such as the emitter type, the density of the particle model, and the initial emission direction of the particle model may be determined. The emitter type may include at least surface, volume, and grid types, and the initial emission direction of the particle model may include at least a fixed direction, a normal direction, and a random direction. Further based on the above control parameters of the determined particle model, initial attribute data such as the speed, independent ID, random value, and life span of the particle model may be determined.

[0093] As an optional embodiment, the control parameters include the emission direction of the particle model from the target model, the initial attribute data include the initial speed of the particle model, and the initial attribute data of the particle model is determined based on the control parameters, including: determining the initial speed of the particle model in the emission direction.

[0094] In this embodiment, the control parameter of the particle model may include the emission direction of the particle model emitted from the target model, and the initial attribute data of the particle model may include the initial velocity of the particle model. After determining at least one particle model emitted from the target model, the emission direction of the particle model emitted from the target model may be determined. Further, in the emission direction of the determined particle model, the initial velocity of the particle model may be determined. The emission direction may be the initial velocity direction of the particle model. The initial velocity of the particle model may be represented by vel.

[0095] Optionally, when the target model emits a particle model, that is, when the particle model is born, an initial velocity vel is assigned to the particle model so that the particle model has an initial motion direction when it starts to move, thereby realizing control of various emission directions of the particle model. For example, the normal direction along the emitter grid model (for example, a spherical grid model) is determined as the emission direction of the particle model, and the initial velocity vel of the particle model can be determined in this emission direction.

[0096] As an optional embodiment, the initial attribute data includes the total target existence time of the particle model. Based on the control parameters, the initial attribute data of the particle model is determined, including: determining the total target existence time of the particle model moving in the emission direction.

[0097] In this embodiment, the initial attribute data of the particle model may include the total target existence time of the particle model. After determining at least one particle model emitted from the target model, the emission direction of the particle model emitted from the target model may be determined. After determining the emission direction emitted by the particle model from the target model, the total target existence time of the particle model moving in the emission direction may be determined. Among them, the total target existence time may be the total time the particle model exists in the particle system, and the total target existence time may be called life time, life length, and may be represented by lifetime, and the unit may be seconds (s). This is only for example, and no specific limitation is imposed on the unit of the total target existence time of the particle model.

[0098] Optionally, after determining the emission direction of the particle model from the target model, the random life length of the particle model moving in the emission direction can be determined. For example, the life length of the particle model can be controlled to fluctuate around 300s, thereby achieving the purpose of enhancing the randomness of the life length of the particle model.

[0099] As an optional embodiment, determining the target total existence time of the particle model moving in the emission direction includes: determining the initial total existence time of the particle model moving in the emission direction; adjusting the initial total existence time using a random time to obtain a target time range; and determining the target total existence time within the target time range.

[0100] In this embodiment, after determining the emission direction of the particle model emitted from the target model, the initial total existence time of the particle model moving in the emission direction can be determined. After determining the initial total existence time of the particle model moving in the emission direction, the initial total existence time of the determined particle model can be adjusted using a random duration to obtain a target duration range. Further, within the obtained target duration range, the target total existence time can be determined. Among them, the initial total existence time can be the total time that the particle model exists in the particle system set in advance according to actual conditions, for example, the initial total existence time can be represented by a. The random duration can be a random length set in advance according to actual conditions, and the random duration can be used to adjust the initial total existence time of the particle model, for example, the random duration can be represented by b%. The target duration range can be the range of the life span of the particle model in the particle system, which can be called a random life span fluctuation range.

[0101] For example, after determining the emission direction of the particle model emitted from the target model, the initial total existence time a of the particle model moving in the emission direction can be determined. Further using the random duration b%, the determined initial total existence time a of the particle model can be adjusted, for example, a+a*(b / 100) is determined as the maximum value of the target duration range, and aa*(b / 100) is determined as the minimum value of the target duration range, thereby obtaining the target duration range. After obtaining the target duration range of the particle model, the target total existence time can be determined within the obtained target duration range of the particle model.

[0102] This embodiment can achieve the effect of enhancing the randomness of the particle system by setting a random value of the life span of the particle model.

[0103] As an optional embodiment, the method further includes: adjusting the distribution results of the particle models corresponding to the total existence durations of different targets within the target duration range.

[0104] In this embodiment, after adjusting the initial total existence time of the determined particle model by using the random duration to obtain the target duration range, the distribution results of the particle models corresponding to different target total existence times within the target duration range can be adjusted. The distribution result can be the distribution result of the number of particle models.

[0105] Optionally, after determining the target duration range of the particle model, the distribution result of the life duration of the particle model within the target duration range can be adjusted through random distribution control. For example, if the random life duration fluctuation range of the particle model is 0 to 600s, the distribution result of the life duration of the particle model can be adjusted to make the distribution of particle models with life durations of 0 and 600s larger, and the distribution of particle models with life durations between 0 and 600s smaller.

[0106] As an optional embodiment, step S206, the first initial attribute data includes the existing duration of the particle model, and the first initial attribute data is adjusted to obtain the first target attribute data, including: adjusting the existing duration to obtain the adjusted existing duration, wherein the first target attribute data includes the adjusted existing duration; adjusting the second initial attribute data based on the first target attribute data to obtain the second target attribute data, including: adjusting the second initial attribute data based on the ratio of the adjusted existing duration to the target total existence duration to obtain the second target attribute data.

[0107] In this embodiment, the first initial attribute data of the particle model may include the existing duration of the particle model. In the process of controlling the movement of the particle model according to the attribute data set, the existing duration of the particle model may be adjusted to obtain the adjusted existing duration. Further based on the duration ratio between the adjusted existing duration and the target total existence duration, the second initial attribute data may be adjusted to obtain the second target attribute data. Among them, the first target attribute data may include the adjusted existing duration. The existing duration may be the duration of the particle model existing in the particle system, that is, the age of the particle model, which may be represented by age. The duration ratio may be the ratio between the adjusted existing duration and the target total existence duration, which may be called the particle model age ratio parameter, which may be represented by life, and the duration ratio may be used to control the linkage relationship between the basic attribute data of the particle model and the life duration.

[0108] Optionally, in the process of controlling the movement of the particle model according to the attribute data set, the age of the particle model is determined by recording the time each particle model has existed. The age of the particle model is further adjusted to obtain the adjusted age. After obtaining the adjusted age, the adjusted age is divided by the life span to obtain the percentage attribute parameter of the current age of each particle model in the total life span, that is, the life span proportion. Finally, based on the obtained life span proportion, the second initial attribute data is adjusted to obtain the second target attribute data.

[0109] As an optional embodiment, the second initial attribute data includes an initial speed. Based on the ratio of the adjusted existing duration to the target total existence duration, the second initial attribute data is adjusted to obtain second target attribute data, including: based on the ratio of the adjusted existing duration to the target total existence duration, the initial speed is adjusted to obtain the target speed, wherein the second target attribute data includes the target speed.

[0110] In this embodiment, the second initial attribute data of the particle model may include an initial speed. After adjusting the existing duration of the particle model to obtain the adjusted existing duration, the initial speed of the particle model is adjusted based on the duration ratio between the adjusted existing duration and the target total existence duration to obtain the target speed. The second target attribute data of the particle model may include the target speed.

[0111] Optionally, after adjusting the age of the particle model to obtain the adjusted age, the adjusted age is divided by the life span to obtain the particle model age ratio parameter life. This embodiment uses the particle model age ratio parameter life to control the speed change of the particle model in the life span. That is, based on the particle model age ratio parameter life, the initial speed of the particle model is adjusted to obtain the target speed.

[0112] For example, the particle model age ratio parameter life is a floating point value between 0 and 1. 0 can be used to represent the life value of the particle model when it is just born, and 1 can be used to represent the life value of the particle model when it dies. When the speed of the particle model changes with the life duration, and the intensity of the speed of the particle model changing with the life duration is pulled to 100%, since the speed of the particle model is 0 when it is just born, as the age of the particle model increases, the speed of the particle model will increase linearly. For example, the speed of the particle model is slow when it is just generated (born), so the density of the particle model in the emitter area will be relatively high, thereby realizing the function of controlling the speed of the particle model to change with the life duration.

[0113] As an optional embodiment, the second initial attribute data includes the initial size of the particle model. Based on the ratio of the adjusted existing duration to the target total existence duration, the second initial attribute data is adjusted to obtain the second target attribute data, including: based on the ratio of the adjusted existing duration to the target total existence duration, the initial size of the particle model is adjusted to obtain the target size, wherein the second target attribute data includes the target size.

[0114] In this embodiment, the second initial attribute data of the particle model may include the initial size of the particle model. After adjusting the existing duration of the particle model to obtain the adjusted existing duration, the initial size of the particle model is adjusted based on the duration ratio between the adjusted existing duration and the target total existence duration to obtain the target size. The second target attribute data may include the target size.

[0115] Optionally, after obtaining the particle model age proportion parameter life, the particle model age proportion parameter life is used to control the size change of the particle model during the life span. That is, based on the particle model age proportion parameter life, the initial size of the particle model is adjusted to obtain the target size, thereby realizing the function of controlling the size of the particle model to change with the life span.

[0116] As an optional embodiment, the method further includes: in response to the existing duration of the particle model being greater than the target total existing duration, deleting the particle model.

[0117] In this embodiment, the first initial attribute data of the particle model includes the existence duration of the particle model. After determining the total existence duration of the target moving in the emission direction of the particle model, when the existence duration of the particle model is greater than the total existence duration of the target, the particle model can be deleted in response to the existence duration of the particle model being greater than the total existence duration of the target.

[0118] Optionally, after determining the lifetime of the particle model moving in the emission direction, the age of the particle model can be compared with the lifetime of the particle model. When the age of the particle model is greater than the lifetime of the particle model, the over-age particle model can be deleted by deleting the geometry node, thereby realizing the lifespan function of the particle model.

[0119] As an optional embodiment, the first initial attribute data includes an initial velocity, and the second initial attribute data includes an initial size of a particle model. Step S206, adjusting the first initial attribute data to obtain first target attribute data, includes: adjusting the initial velocity to obtain a target velocity, wherein the first target attribute data includes the target velocity; adjusting the second initial attribute data based on the first target attribute data to obtain second target attribute data, includes: adjusting the initial size of the particle model based on the target velocity to obtain a target size, wherein the second target attribute data includes the target size.

[0120] In this embodiment, the first initial attribute data of the particle model may include an initial speed, and the second initial attribute data of the particle model may include an initial size. In the process of controlling the movement of the particle model according to the attribute data set, the initial speed of the particle model may be adjusted to obtain a target speed. Further based on the obtained target speed, the initial size of the particle model may be adjusted to obtain a target size. Among them, the first target attribute data of the particle model may include a target speed. The second target attribute data of the particle model may include a target size.

[0121] Optionally, after determining the initial speed of the particle model, the initial speed of the particle model can be adjusted to obtain a target speed. Further based on the obtained target speed, the initial size of the particle model can be adjusted to obtain a target size, thereby realizing the function of controlling the size of the particle model to change with speed.

[0122] As an optional embodiment, the initial size of the particle model is adjusted based on the target speed to obtain the target size, including: separating sub-speeds corresponding to multiple color channels from the target speed; adjusting the initial size using the sub-speeds; and merging multiple adjusted initial sizes corresponding to the multiple color channels to obtain the target size.

[0123] In this embodiment, after adjusting the initial velocity of the particle model to obtain the target velocity, the sub-velocities corresponding to the multiple color channels can be separated from the obtained target velocity. After separating the sub-velocities corresponding to the multiple color channels from the target velocity, the obtained sub-velocities are used to adjust the initial size. The multiple adjusted initial sizes corresponding to the multiple color channels are further merged to obtain the target size. Among them, the color channel can include at least a red (Red, referred to as R) channel, a green (Green, referred to as G) channel and a blue (Blue, referred to as B) channel. The target size can be represented by a scale.

[0124] Optionally, after adjusting the initial speed of the particle model to obtain the target speed, the three color channels of the target speed can be separated and controlled separately, that is, the sub-speeds corresponding to the three color channels are separated from the target speed. The separated sub-speeds are further added to the initial size of the particle model, and the added results are merged again to obtain the target size scale of the particle model.

[0125] As an optional embodiment, adjusting the initial speed to obtain the target speed includes: adjusting the initial speed using a random speed to obtain a target speed range; and determining the target speed within the target speed range.

[0126] In this embodiment, the second initial attribute data of the particle model may include an initial speed. The initial speed may be adjusted using a random speed to obtain a target speed range. Further, within the obtained target speed range, a target speed may be determined. The random speed may be a random speed pre-set according to actual conditions, and the random speed may be used to adjust the initial speed of the particle model. The target speed range may be a speed range of the particle model, for example, the target speed range may be (0, 1), which is only for illustration and does not specifically limit the target speed range.

[0127] Optionally, the minimum and maximum values ​​of the initial velocity in a single color channel are found through a normalization algorithm of vector operations, and the minimum and maximum values ​​of the initial velocity are mapped to values ​​between 0 and 1, that is, the initial velocity is adjusted using a random velocity to obtain a target velocity range (0, 1), thereby stripping the velocity of each particle model and extracting only its direction vector data. Further, the scaling of vector operations is used to uniformly control the velocity of each particle model to determine the target velocity within the target velocity range.

[0128] In the prior art, it is difficult for the built-in particle system of Blender to accurately control the speed of the particle model. Once a force field is added, the speed of the particle model will be interfered with, resulting in an out-of-control particle model. However, this embodiment stores the speed of each particle model by overwriting the stored named attribute node, thereby achieving unified management of the speed of the particle model, and thus achieving the effect of accurately controlling the position and speed of the particle model.

[0129] As an optional embodiment, the initial velocity is adjusted to obtain the target velocity, including: determining a tangent at a position where the distance from the particle model is less than a distance threshold on a geometric curve associated with the particle model; and adjusting the velocity direction of the initial velocity using the slope of the tangent to obtain the target velocity.

[0130] In this embodiment, the second initial attribute data of the particle model may include an initial velocity. On a geometric curve associated with the particle model, a tangent at a position where the distance from the particle model is less than a distance threshold may be determined. After determining the tangent at a position where the distance from the particle model is less than the distance threshold, the speed direction of the initial velocity may be adjusted using the slope of the tangent to obtain a target velocity. The geometric curve may be referred to as a guide curve or a spline. The distance threshold may be a threshold set according to actual conditions. The slope of the tangent may be represented by a tagent tangent value.

[0131] Optionally, on the guide curve associated with the particle model, a tangent at a position where the distance to the particle model is less than a distance threshold is determined, for example, a tangent closest to the particle model is determined. The speed direction of the initial speed is further adjusted using the slope of the tangent, that is, using the tagent tangent value, for example, the adjustment method can be to add the obtained tagent tangent value to the speed direction of the initial speed, thereby obtaining the target speed of the particle model.

[0132] For example, the tagent tangent value of the guide curve closest to the point cloud particle model position is obtained by sampling the nearest node and the sampling number node. The tagent tangent value of the guide curve obtained is added to the speed direction of the initial speed of the particle model to change the speed direction of the particle model, thereby achieving the purpose of guiding the direction of the particle model.

[0133] As an optional embodiment, the method further includes: in response to the distance between the particle model and the geometric curve being within a target distance range, controlling the movement of the particle model; and / or controlling the movement of the particle model using noise of the particle model.

[0134] In this embodiment, after sampling the distance between the geometric curve and the particle model, when the distance between the particle model and the geometric curve is within the target distance range, the particle model movement can be controlled in response to the distance between the particle model and the geometric curve being within the target distance range. And / or the particle model movement can be controlled by using the noise of the particle model. The target distance range can be the influence range of the curve vector field.

[0135] Optionally, the distance between the guide curve and the particle model is sampled through a geometric approach node. After sampling the distance between the guide curve and the particle model, the influence range of the curve vector field is set. When the distance between the particle model and the geometric curve is within the influence range of the set curve vector field, the movement of the particle model can be controlled to achieve the purpose of strengthening the control of the particle model.

[0136] Optionally, when the noise of the particle model is used to control the movement of the particle model, the color value output by the noise texture node can be the noise continuously distributed in the three-dimensional space, and the irregular vector characteristics of the noise can be used to control the random disorder of the particle model movement. At the same time, the independent ID attribute of each particle model is used to control the W evolution value of the noise texture node. Among them, the W evolution value can be a floating point value used by the noise texture node to control the noise distribution style. By increasing or decreasing the floating point value, the distribution of the noise can be changed continuously and smoothly. That is, through this feature, using the independent ID attribute, the spatial noise distribution of each particle model can be different.

[0137] As an optional embodiment, determining initial attribute data associated with the movement process of the particle model includes at least one of the following: determining identification information of the particle model during the movement process; and / or determining a random value of the particle model during the movement process, wherein the random value is used to determine the material of the particle model.

[0138] In this embodiment, after determining at least one particle model emitted from the target model, the identification information of the particle model during the movement process can be determined. And / or the random value of the particle model during the movement process can be determined. Among them, the identification information can be a unique ID number parameter of the particle model, which can be used to distinguish each particle model and can be called an independent ID. The random value can be a random floating-point attribute parameter between 0 and 1, which can be used to determine the material of the particle model and can be represented by random.

[0139] Optionally, the random seed of each frame of the random value node can be changed through the scene time node to set an independent ID for the particle model generated for each frame. In addition, a random value node can be used to set a floating point attribute parameter random between 0 and 1 for each particle model, which can be used to control the random transparency of the particle.

[0140] As an optional embodiment, determining the attribute data set based on the initial attribute data includes: determining target attribute data required by the particle model; and adding the target attribute data to the initial attribute data to obtain the attribute data set.

[0141] In this embodiment, after determining the initial attribute data associated with the motion process of the particle model, the target attribute data required by the particle model can be determined. Further, the target attribute data can be added to the initial attribute data to obtain an attribute data set. The target attribute data can be attribute data of the particle model newly added according to actual conditions.

[0142] In the related art, the control parameters related to the material of the particle model are fixed, and it is not possible to add related parameter attributes according to the needs of different projects. However, in addition to setting various basic attributes of the particle model, such as speed (initial speed), independent ID, random value and life span, this embodiment can also add and set new other attribute data according to the needs of the project, and can freely create new parameter attributes.

[0143] As an optional embodiment, step S208, using the first target attribute data and the second target attribute data, dynamically renders and displays the particle model to obtain the performance result of the particle model emitted from the target model, including: determining the first target material associated with the first target attribute data and the second target attribute data; using the first target material, dynamically renders and displays the particle model to obtain the performance result of the particle model emitted from the target model.

[0144] In this embodiment, after adjusting the first initial attribute data to obtain the first target attribute data, and adjusting the second initial attribute data based on the first target attribute data to obtain the second target attribute data, the first target material associated with the first target attribute data and the second target attribute data can be determined. The particle model is further dynamically rendered and displayed using the determined first target material to obtain the performance result of the particle model emitted from the target model. The first target material can be the material associated with the first target attribute data and the second target attribute data of the particle model.

[0145] Optionally, the available attribute parameters output by the particle system are associated with the material of the particle model, that is, the first target material associated with the first target attribute data and the second target attribute data can be determined. The particle model is further dynamically rendered and displayed using the determined first target material to obtain the performance result of the particle model emitted from the target model.

[0146] As an optional embodiment, step S208, using the first target attribute data and the second target attribute data, dynamically renders and displays the particle model to obtain the performance result of the particle model emitted from the target model, including: obtaining the second target material of the target model; using the second target material, the first target attribute data and the second target attribute data, dynamically renders and displays the particle model to obtain the performance result of the particle model emitted from the target model.

[0147] In this embodiment, after adjusting the first initial attribute data to obtain the first target attribute data, and adjusting the second initial attribute data based on the first target attribute data to obtain the second target attribute data, the second target material of the target model can be obtained. The particle model is further dynamically rendered and displayed using the obtained second target material of the target model, as well as the first target attribute data and the second target attribute data of the particle model, to obtain the performance result of the particle model emitted from the target model. The second target material is the material of the particle model, which can be used to inherit the emitter map material.

[0148] Optionally, use the property node of the material system to read the texture coordinates (UV) of the emitter mesh model. After changing the UV matching type to instance, assign the material to the particle system, and pass the material information of the corresponding position of the emitter directly to the particle model according to the UV positioning, so that the particle model material inherits the emitter map material.

[0149] In an embodiment of the present disclosure, at least one particle model emitted from a target model is determined; an attribute data set of the particle model is determined, wherein the attribute data set includes at least first initial attribute data and second initial attribute data that are mutually related; in the process of controlling the movement of the particle model according to the attribute data set, the first initial attribute data is adjusted to obtain first target attribute data, and the second initial attribute data is adjusted based on the first target attribute data to obtain second target attribute data; the particle model is dynamically rendered and displayed using the first target attribute data and the second target attribute data to obtain a performance result of the particle model emitted from the target model. That is, in the process of controlling the movement of the particle model according to the attribute data set, the first initial attribute data is adjusted to obtain first target attribute data, and the second initial attribute data is adjusted based on the first target attribute data to obtain second target attribute data, that is, linkage control between the first target attribute data and the second target attribute data is realized, and the particle model is dynamically rendered and displayed using the first target attribute data and the second target attribute data to obtain a performance result of the particle model emitted from the target model, thereby achieving the technical effect of improving the effect of model rendering and solving the technical problem of poor effect of model rendering.

[0150] The technical solution of the embodiment of the present disclosure is further introduced below with examples in combination with preferred implementation modes.

[0151] When using Blender for CG creation, particle effects are usually required, especially the frequently appearing Mars, dust and ribbon particles. Blender is used to provide a series of animation short film production solutions from modeling, animation, materials, rendering, audio processing and video editing. However, the functions and controllability of the particle system that comes with Blender are relatively simple. For example, the precise positioning and flow direction guidance of the particle model cannot meet the actual work needs, and there is no simple, easy-to-use and versatile particle system yet.

[0152] In the prior art, when particle special effects are completed through Blender, the particle system provided by the software is usually used. Figure 3 FIG. 1 is a schematic diagram of using a particle system provided by the software to achieve particle special effects according to one embodiment of the present disclosure, such as Figure 3As shown, first create the emitter mesh, then select the particle model in the property panel, click the "+" symbol to add the particle system, and finally debug the particle system panel parameters. This solution is built into the Blender software, and the particle system effect is achieved by debugging the panel parameters at each level.

[0153] However, the above method only has relatively basic particle model attribute settings, such as speed, rotation, size and life, etc., lacks more advanced and detailed parameter settings and controls for these attributes, and cannot realize the linkage control of the particle model's speed and the particle model's life, and cannot realize the linkage control of the particle model's size and the particle model's speed. The material-related control parameters of the particle model are fixed, for example, there are only particle information such as number, randomness, life span, life cycle, position, size, speed and angular velocity, and related parameter attributes need to be added according to different projects. At the same time, the material attributes of the particle model can only be used in the production-level renderer (Cycles), and cannot be used in the more efficient real-time renderer (EEVEE). Among them, the Cycles renderer is a physically based production-level renderer developed by the Blender project, which is used to provide easy-to-use and accurate physical rendering results. The EEVEE renderer is a real-time rendering engine built into Blender for fast preview and real-time interactive editing. In addition, the above methods lack accurate and effective guidance and control of particle models. Although Blender comes with various simulated force fields, such as constant force, wind force, eddy current, magnetic force, resonance, charge, Lanner Jones molecular force, texture, curve guidance, cluster, turbulence, drag and fluid flow, which can guide particle flow to a certain extent, these force fields themselves lack more detailed and precise setting parameters, resulting in the inability to guide particle models in an advanced way. For example, it is impossible to use the tangent vectors of multiple curves to accurately divert and guide particle models, and the particle system is a fixed built-in function and lacks customizability.

[0154] In order to solve the above problems, the present invention utilizes the Blender geometry node function to create a highly versatile, multifunctional and controllable particle system. Figure 4 is a schematic diagram of a controllable parameter adjustment panel of a particle system according to one embodiment of the present disclosure, such as Figure 4 As shown in the figure, the particle system has a complete and multifunctional controllable parameter adjustment panel, including three major control panels: emitter, particle attributes, and force field, and is equipped with settings for the linkage changes between parameter attributes. Among them, the emitter panel can at least set the emitter parameters, the particle attribute panel can at least set the basic attribute data of the particle model, particle size, particle rotation and other parameters, and the force field panel can at least set the parameters related to the noise vector field and curve vector field.

[0155] Figure 5is a schematic diagram of an inherited emitter map material according to one embodiment of the present disclosure, such as Figure 5 As shown, the material of the particle model of this embodiment is fully adapted to the EEVEE renderer. In addition to having basic material attribute parameter outputs, new parameter attributes can be freely created according to different projects, and the emitter map material can be directly and accurately inherited.

[0156] Figure 6 is a schematic diagram of the flow direction of the guided particle model according to one embodiment of the present disclosure, such as Figure 6 As shown, this embodiment uses curves (splines), noise attributes, etc. to generate vector fields to accurately guide the flow direction of the particle model. In addition, the particle system is highly customizable. More types of vector field controls can be added according to project requirements, and a certain degree of physical collision and fluid simulation modules can also be developed.

[0157] This example uses Blender's geometry node simulation node system to build a new particle system from scratch. Figure 7 is a schematic diagram of a geometric node of Blender according to one embodiment of the present disclosure. Figure 8 is a schematic diagram of a simulation node area according to one embodiment of the present disclosure, such as Figure 8 As shown, the particle system may at least include an emitter, particle basic property settings, a particle system core, particle transformation property control, particle instantiation, material assignment, and implementation instances. Figure 8 The middle dotted area is the simulation node area. The main function of Blender's simulation node group is to continuously iterate and accumulate data entering the simulation node area. Fig. 9 is a schematic diagram of a basic particle system according to one embodiment of the present disclosure, such as Fig. 9 As shown, create an emitter grid, convert the grid into points, and finally merge the point data. By simulating this feature of the node, you can simply build a basic particle system.

[0158] Fig.10 is a schematic diagram of simulating node area merging point data according to one embodiment of the present disclosure, such as Fig.10 As shown, under the above basic particle system, for example, at least including assigning particle age, normalizing particle age, setting curve vector field, setting noise vector field, speed size control, deleting over-age particles, particle size control, rotation control and particle instance, etc., each frame of point data converted from the emitter grid will be merged and accumulated through the simulation node area, such as Fig.10 As shown in the dotted area in the middle, based on this particle system, by modifying and adding various module parameters, you can accurately control and guide these accumulated particle models.

[0159] Fig.11is a schematic diagram of setting a curve vector field and setting a noise vector field according to one embodiment of the present disclosure, such as Fig.11 As shown, the module functions of setting curve vector field and setting noise vector field guidance are the most core and widely used. There are also many other functions such as particle life control, size control, rotation control, material parameter conduction control, etc.

[0160] Fig.12 is a schematic diagram of the composition structure of a particle system according to one embodiment of the present disclosure, such as Fig.12 As shown, the particle system is composed of an emitter module, a particle basic attribute module, a particle system core module, a vector field control module, a particle transformation attribute control module, and material and instantiation related modules. Among them, the particle basic attribute module can be called a particle basic attribute setting module, and the particle basic attribute setting module can at least include setting the initial velocity direction, setting an independent ID, setting a particle random value, and setting a life span. The particle system core module can at least include assigning velocity, assigning particle age, normalizing particle age, velocity size control, and deleting over-age particles. The vector field control module can at least include setting a curve vector field and setting a noise vector field. The particle transformation attribute control module can at least include size control and rotation control. The material and instantiation related modules can at least include particle instances, particle instantiations, material assignments, and implementation instances. As shown in FIG. Fig.12 As shown, the particle system continuously generates particles by running each module from top to bottom.

[0161] The following is a further introduction to the core functions of the particle system, including the core parts of the particle system, vector field control, particle basic attribute linkage control, particle attribute parameter control material, etc.

[0162] The main function of the transmitter module is to set the transmitter type and transmission direction. Fig.13 is a schematic diagram of a transmitter module according to one embodiment of the present disclosure, such as Fig.13 As shown, the emitter node can set the particle density, emitter grid, emitter type and random seed. The emission direction can at least include fixed direction emission, emission along the grid normal direction and random direction emission. Fig.13 The figure shows the connection between the normal of the emitter and the normal input, indicating that the emission direction is along the normal direction of the grid. It should be noted that the functions of the emitter module are relatively conventional, and are only made as functional modules to enhance convenience, in which each node group is originally assembled. The main control parameters of the emitter module presented on the operation panel may at least include but are not limited to: emitter name, type, particle density, emission direction, emission intensity in the coordinate axis direction, emission intensity in the direction of the specified object, and emission intensity in the normal direction of the specified object.

[0163] Fig.14is a schematic diagram of the internal structure of a transmitter node according to one embodiment of the present disclosure, such as Fig.14 As shown, the emitter node group encapsulates multiple functional nodes, which are mainly used to control the emitter type and particle density. There are three emitter types (surface, volume, and grid) to choose from. In addition, the emission direction node group is mainly used to control the initial emission direction of particles. There are three emission direction types (fixed direction, normal direction, and random direction) that can be matched.

[0164] The particle basic attribute module is mainly used to set various basic attributes of particles, such as speed (initial speed), independent ID, random value, life span, etc., and other new attribute data can be added and set according to project needs. The various attribute data set by this module is for the purpose of subsequent enhancement of the control of the particles themselves, and it is a relatively important module in the particle system.

[0165] The speed (initial speed) of the particle model can be expressed by vel. Set vel as the speed parameter of the particle model. The initial speed here is mainly set in conjunction with the emission direction of the emitter module. Later, a speed control module is established in the particle core module. Fig.15 is a schematic diagram of setting the initial velocity direction of a particle model according to one embodiment of the present disclosure, such as Fig.15 As shown, the various emission direction controls in the emitter module are essentially to give the particle model an initial velocity vel vector parameter value when it is born, so that the particle model has an initial movement direction when the particle system starts running. Fig.16 is a schematic diagram of an emission particle model according to one embodiment of the present disclosure, such as Fig.16 As shown, a spherical grid is used as the emitter, and the emission direction is set to emit the particle model along the normal direction of the emitter grid. The vel velocity vector value is expressed in red, green, and blue (RGB for short), which can be used in the material system to control the color material of the particle model as the speed changes.

[0166] Fig.17 is a schematic diagram of setting an independent ID according to one embodiment of the present disclosure, such as Fig.17 As shown, this embodiment changes the random seed of each frame of the random value node (Random Value) through the scene time node (Scene Time), and sets a unique ID number parameter for the particle model generated for each frame based on the set ID node (Set ID). The set ID number parameter can be used to distinguish each particle model separately later.

[0167] Fig.18 is a schematic diagram of setting random values ​​of a particle model according to one embodiment of the present disclosure, such as Fig.18As shown, this embodiment uses a random value node (Random Value) to set a random floating point attribute parameter from 0 to 1 for each particle model, and the set random value can be represented by random. Fig.19 is a schematic diagram of controlling the random transparency of a particle model based on a random value of the particle model according to one embodiment of the present disclosure, such as Fig.19 As shown, the random value of the particle model is widely used in particle materials and can control the random transparency of the particle model.

[0168] In a particle system, setting the life span of each particle model is the most basic and important attribute. The life span of a particle model can be represented by lifetime. Fig. 20 is a schematic diagram of setting the life span of a particle model according to one embodiment of the present disclosure, such as Fig. 20 As shown, it can include a random life duration module and a random distribution control module, and the random life duration module includes a random node_float node group.

[0169] Fig.21 is a schematic diagram of controlling the random value of life length according to one embodiment of the present disclosure, such as Fig.21 As shown in the figure, the function of freely controlling the random value of life length can be achieved through the random node_float node group, for example, using the group input node, mapping range node, addition node, subtraction node, absolute value node, random value node and group output node. Assuming that the life length is set to 300, when the random value reaches 100, the random life length fluctuation range of the particle model reaches the highest 0~600.

[0170] Fig. 22 is a schematic diagram of a random distribution control module according to one embodiment of the present disclosure, such as Fig. 22 As shown, the random life span can control the distribution of the life span of each particle model through a subsequent random distribution control module, wherein the random distribution control module includes a mapping range node and a floating point curve node.

[0171] Fig.23 is a schematic diagram of a random distribution control curve according to one embodiment of the present disclosure, wherein the random distribution control curve is adjusted as follows Fig.23 The form shown will cause more particle models with life spans of 600 and 0 to be distributed, and less particle models with life spans between 0 and 600.

[0172] The core module of the particle system can realize functions such as how to drive particles with the velocity attribute vel, how to control the killing of particles, and how to control the speed size in real time. Fig.24 is a schematic diagram of a particle system core module according to one embodiment of the present disclosure, such as Fig.24 As shown, the core module of the particle system may at least include assigning speed, assigning particle age, normalizing particle age, speed size control, and deleting over-age particles.

[0173] According to the engine of the particle system, the simulation node group can be used to continuously and iteratively accumulate the characteristics of the data entering the simulation node area, so as to give speed and drive particles. Fig.25 is a schematic diagram of assigning velocity to a particle model according to one embodiment of the present disclosure, such as Fig.25 As shown, it includes a named attribute node and a set position node. By setting the position node, the vel vector value is continuously accumulated and assigned to each particle in a loop to drive the particle model to move along the direction of the speed vel vector value.

[0174] This embodiment realizes the particle lifespan function based on the particle system by controlling the killing of particle models whose lifespan exceeds a preset lifetime. Fig.26 is a schematic diagram of a control killing particle model according to one embodiment of the present disclosure, such as Fig.26 As shown, first, the age attribute age is assigned to each particle model through the named attribute node. Age is accumulated by +1 in each frame through the simulation area through the addition math node to record the existence time of each particle model. By dividing the age age and the life span lifetime, the attribute parameter of the percentage of the current age of each particle model to the total life span is obtained. This attribute parameter can be represented by life. The attribute parameter life will be widely used to control the linkage between various basic attributes of the particle model and life. When it is necessary to delete the over-age particle model, use the comparison node to compare age and lifetime. For example, if the age of the particle model is greater than the corresponding lifetime, the over-age particle model can be deleted by deleting the geometry node to realize the life span function of the particle model.

[0175] Blender's existing built-in particle system makes it difficult to accurately control the speed of particle models. As long as a force field is added, the speed of the particle model will be interfered with, resulting in an uncontrolled particle model. This is usually something designers don't want to see, because in actual application scenarios, designers would rather control the position and speed of the particle model than the unpredictable physical force field.

[0176] Fig. 27 is a schematic diagram of controlling the velocity of a particle model according to one embodiment of the present disclosure, such as Fig. 27As shown in the figure, the speed of each particle model is overwritten by storing the named attribute node to achieve unified management of the particle model speed. Through the normalization algorithm of vector operation, the minimum and maximum values ​​in a single channel are found, and then mapped to a value between 0 and 1, so as to strip the speed of each particle model and only extract its direction vector data. Secondly, the speed of each particle model is individually and uniformly controlled by scaling the vector operation.

[0177] The particle model speed control module includes a random speed control module and a speed change function module. Fig.28 is a schematic diagram of a random speed control module according to one embodiment of the present disclosure, such as Fig.28 As shown, the functional principle of this module is the same as the random function of the lifetime parameter lifetime, and it reuses the random node _float and random distribution control.

[0178] Fig.29 is a schematic diagram of a speed-with-life-change function module according to one embodiment of the present disclosure, such as Fig.29 As shown, it includes a group input node, a named attribute node, a floating point curve node, and a mapping range node. The speed changes with life function module controls the speed change of the particle model during the life span through the particle age ratio parameter life.

[0179] Fig.30 is a schematic diagram of the change of particle model speed with life according to one embodiment of the present disclosure, such as Fig.30 As shown, after the speed changes with life intensity to 100%, the default is as follows Fig.29 The curve shown in the figure is set. When the particle model is just born, the speed is 0. As the age increases, the speed will increase linearly, such as Fig.30 As shown, the particle model is emitted from the spherical area of ​​the origin, and the initial velocity is moving upward. Since the speed is slow at birth, the density of the emitter area will be relatively high.

[0180] Fig.31 is a schematic diagram of a vector field control module according to one embodiment of the present disclosure, such as Fig.31 As shown, the vector field control module includes two functional modules: curve vector field and noise vector field. Fig.32 is a schematic diagram of a curve vector field module according to one embodiment of the present disclosure, such as Fig.32As shown, it includes the sampling curve tangent vector value and the influence range part, wherein the sampling curve tangent vector value includes the curve conversion point node, the sampling nearest node and the sampling number node, and the influence range includes the geometric proximity node and the mapping range node. In addition, it also includes the object information node, the scaling node, the guide speed direction node, the storage named attribute node and the group output node. In addition, the relevant parameters involved in the curve vector field module include the guide curve, the guide curve accuracy, the influence range and the guide strength.

[0181] The control of the vector field by the curve vector field module is essentially to control the vector direction of the overriding velocity parameter vel, such as Fig.32 The example stores named attribute nodes. This embodiment obtains the guide curve closest to the point cloud particle model position by sampling the nearest node (Sample Nearest) and the sampling index node (Sample Index). Fig.32 The tangent value of the curve and object information node shown in the figure is added to the current particle velocity vector to change its velocity direction to achieve the purpose of guiding the direction. Fig.33 is a schematic diagram of the influence range of the curve vector field according to one embodiment of the present disclosure, Fig.34 is a schematic diagram of the influence range of another curve vector field according to one embodiment of the present disclosure, such as Fig.33 and Fig.34 As shown in the figure, the distance between the guide curve and the particle model is sampled through the Geometry Proximity node, and the influence range of the curve vector field is set to further strengthen the control.

[0182] Fig.35 is a schematic diagram of a noise vector field module according to one embodiment of the present disclosure, such as Fig.35 As shown, discontinuity is set through ID nodes, multiplication nodes and addition nodes. In addition, it also includes noise texture nodes, subtraction nodes, scaling nodes, guide velocity direction nodes and storage named attribute nodes. In addition, the relevant parameters involved in the noise vector field module include force field strength, scaling, details, roughness, distortion, noise evolution, gaps and discontinuities.

[0183] Fig.36 is a schematic diagram of controlling random disorder of particle motion according to one embodiment of the present disclosure, such as Fig.36 As shown, the color value output by the noise texture node is a continuously distributed noise in three-dimensional space, and its irregular vector characteristics are used to control the random disorder of the particle model movement.

[0184] Fig.37 is a schematic diagram of continuous noise control according to one embodiment of the present disclosure, Fig.38is a schematic diagram of low-continuity noise control according to one embodiment of the present disclosure, such as Fig.37 and Fig.38 As shown in the figure, by using the independent ID attribute of each particle model and controlling the W evolution value of the noise texture node, the spatial noise controlling each particle model can be different, thereby effectively controlling whether a unified spatial noise is needed to continuously control each particle model. Among them, low-continuity noise control will make the overall particle model movement appear more scattered, because the noise vector field controlling each particle model is relatively independent.

[0185] The particle transformation attribute control module mainly controls the rotation and size of each particle model by instantiating the rotation and scaling of the node at the point. Fig.39 is a schematic diagram of a particle transformation attribute control module according to one embodiment of the present disclosure, such as Fig.39 As shown, it includes size control and rotation control. Among them, the relevant parameters involved in the particle model size include size, random size, random seed, size changes with life and changes with speed. The relevant parameters involved in the particle model rotation include rotation angle, random rotation angle, random seed, camera-facing strength, camera-facing rotation angle, camera-facing random rotation angle, rotation angle along the speed direction, rotation speed, random rotation speed and random seed, etc. In addition to controlling the rotation and size of the particle model, this embodiment also adds various functions of linking other attribute parameters to achieve more quick and convenient effects, such as particle model size changes with speed, particle model size changes with life, particle model rotation facing the camera and particle model rotation along the speed direction, etc.

[0186] The following is a further introduction to the change of particle model size with speed. It should be noted that the principles of particle model size changing with life, particle model rotation facing the camera and particle model rotation along the speed direction are similar to the change of particle model size with speed, and will not be repeated here.

[0187] Fig.40 is a schematic diagram of the size of a particle model according to one embodiment of the present disclosure changing with speed, such as Fig.40 As shown, it includes a group input node, a named attribute node, a length node, a separation XYZ node, a multiplication node, an addition node and a merge XYZ node. This embodiment extracts the speed value of the speed parameter vel, and then controls the three channels of the separation vector (i.e., the X, Y, and Z channels) respectively, and after adding them to the size of the input particle model, they are merged into a loss value again, and the particle model size parameter is re-assigned. The re-assigned particle model size parameter can be represented by scale to achieve the control of the size of the particle model changing with the speed. Among them, the size of the particle model can be represented by x. That is, Fig.40As shown, the size of the particle model is input and processed by a series of processes such as the Separate XYZ node, and then merged using the Merge XYZ node to output the size of the particle model after the size changes with the speed.

[0188] Fig.41 FIG. 1 is a schematic diagram showing the effect of the size of a particle model changing with speed according to one embodiment of the present disclosure. Fig.41 As shown, after the control is turned on, the size x of the particle model increases with the speed.

[0189] In this embodiment, the material of the particle model is also an important part of the final effect presentation, and the material-related control is related to the attribute parameters output by the particle system. The parameters that can be output by this embodiment at present can at least include age, the percentage of the particle's current age in the total life span, life span, random value, rotation, particle size scale and speed vel. Since the particle system in this embodiment is based on modular construction, compared with the built-in fixed particle system, the particle system in this embodiment can output additional parameters at any time according to project requirements, which is more flexible.

[0190] Fig.42 is a schematic diagram of a particle model material inheriting an emitter map material according to one embodiment of the present disclosure, such as Fig.42 As shown, in addition to the above-mentioned material association function, this embodiment can also conveniently allow the material of the particle model to inherit the emitter map material, use the attribute node (attribute) of the material system to read the texture coordinates (UV) of the emitter grid, and after changing the UV matching type to instance, assign the material to the particle system, and pass the material information of the corresponding position of the emitter directly to the particle model according to the UV positioning.

[0191] The following is a further introduction to specific project cases implementing this embodiment.

[0192] Fig.43 is a schematic diagram of a particle model guided by curve control according to one embodiment of the present disclosure, such as Fig.43 As shown, draw the guide curve, increase the guide strength of the curve, and emit the particle model, thereby realizing that the particle model is guided by the curve. The relevant parameters of the curve vector field involved in this step include the guide curve, the guide curve accuracy, the influence range and the guide strength. Fig.44 is a schematic diagram of the final rendering effect according to one embodiment of the present disclosure, such as Fig.44 As shown in the figure, after setting the parameter values ​​of other particle basic attributes, the final rendering effect is obtained.

[0193] In an embodiment of the present disclosure, at least one particle model emitted from a target model is determined; an attribute data set of the particle model is determined, wherein the attribute data set includes at least first initial attribute data and second initial attribute data that are mutually related; in the process of controlling the movement of the particle model according to the attribute data set, the first initial attribute data is adjusted to obtain first target attribute data, and the second initial attribute data is adjusted based on the first target attribute data to obtain second target attribute data; the particle model is dynamically rendered and displayed using the first target attribute data and the second target attribute data to obtain a performance result of the particle model emitted from the target model. That is, in the process of controlling the movement of the particle model according to the attribute data set, the first initial attribute data is adjusted to obtain first target attribute data, and the second initial attribute data is adjusted based on the first target attribute data to obtain second target attribute data, that is, linkage control between the first target attribute data and the second target attribute data is realized, and the particle model is dynamically rendered and displayed using the first target attribute data and the second target attribute data to obtain a performance result of the particle model emitted from the target model, thereby achieving the technical effect of improving the effect of model rendering and solving the technical problem of poor effect of model rendering.

[0194] Through the description of the above implementation methods, those skilled in the art can clearly understand that the method according to the above embodiment can be implemented by means of software plus a necessary general hardware platform, and of course can also be implemented by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present disclosure, or the part that contributes to the relevant technology, can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, a disk, or an optical disk), and includes a number of instructions for a terminal device (which can be a mobile phone, a computer, a server, or a network device, etc.) to execute the methods of each embodiment of the present disclosure.

[0195] In the present embodiment, a model information processing device is also provided, which is used to implement the above-mentioned embodiments and preferred implementation modes, and the descriptions that have been made will not be repeated. As used below, the term "unit" can implement a combination of software and / or hardware of a predetermined function. Although the device described in the following embodiments is preferably implemented in software, the implementation of hardware, or a combination of software and hardware is also possible and conceived.

[0196] Fig.45 is a structural block diagram of an information processing device according to a model of one embodiment of the present disclosure, such as Fig.45 As shown, the information processing device 4500 of the model may include: a first determination unit 4502 , a second determination unit 4504 , an adjustment unit 4506 and a rendering unit 4508 .

[0197] The first determining unit 4502 is used to determine at least one particle model emitted from the target model.

[0198] The second determining unit 4504 is used to determine an attribute data set of the particle model, wherein the attribute data set at least includes first initial attribute data and second initial attribute data that are related to each other.

[0199] The adjustment unit 4506 is used to adjust the first initial attribute data to obtain first target attribute data during the process of controlling the movement of the particle model according to the attribute data set, and adjust the second initial attribute data based on the first target attribute data to obtain second target attribute data.

[0200] The rendering unit 4508 is used to dynamically render and display the particle model using the first target attribute data and the second target attribute data to obtain a performance result of the particle model emitted from the target model.

[0201] Optionally, the second determination unit includes: a first determination module, used to determine initial attribute data associated with the motion process of the particle model, wherein the initial attribute data includes first initial attribute data and second initial attribute data; and a second determination module, used to determine the attribute data set based on the initial attribute data.

[0202] Optionally, the first determination module includes: a first determination submodule, used to determine control parameters of the particle model, wherein the control parameters are used to control the target model to emit the particle model; and a second determination submodule, used to determine initial attribute data of the particle model based on the control parameters.

[0203] Optionally, the control parameter includes an emission direction of the particle model from the target model, the initial attribute data includes an initial velocity of the particle model, and the second determination submodule is further used to determine the initial velocity of the particle model in the emission direction.

[0204] Optionally, the initial attribute data includes a total target existence time of the particle model, and the second determination submodule is further used to determine a total target existence time of the particle model moving in the emission direction.

[0205] Optionally, the second determination submodule is used to determine the target total existence time of the particle model moving in the emission direction, including: determining the initial total existence time of the particle model moving in the emission direction; using a random duration to adjust the initial total existence time to obtain a target duration range; and determining the target total existence time within the target duration range.

[0206] Optionally, the device further includes: a first adjustment unit, configured to adjust distribution results of particle models corresponding to total existence durations of different targets within the target duration range.

[0207] Optionally, the first initial attribute data includes the existing duration of the particle model, and the adjustment unit 4506 is used to adjust the first initial attribute data to obtain first target attribute data, including: a first adjustment module, used to adjust the existing duration to obtain the adjusted existing duration, wherein the first target attribute data includes the adjusted existing duration; the adjustment unit 4506 is used to adjust the second initial attribute data based on the first target attribute data to obtain second target attribute data, including: a second adjustment module, used to adjust the second initial attribute data based on the ratio of the adjusted existing duration to the target total existence duration to obtain the second target attribute data.

[0208] Optionally, the second initial attribute data includes an initial speed, and the second adjustment module includes: a first adjustment submodule, used to adjust the initial speed based on the ratio of the adjusted existing duration to the target total existence duration to obtain a target speed, wherein the second target attribute data includes the target speed.

[0209] Optionally, the second initial attribute data includes an initial size of the particle model, and the second adjustment module includes: a second adjustment submodule, used to adjust the initial size of the particle model based on the ratio of the adjusted existing duration to the target total existence duration to obtain the target size, wherein the second target attribute data includes the target size.

[0210] Optionally, the device further comprises: a deleting unit, configured to delete the particle model in response to the existing duration of the particle model being greater than the target total existence duration.

[0211] Optionally, the first initial attribute data includes an initial velocity, the second initial attribute data includes an initial size of the particle model, and the adjustment unit 4506 is used to adjust the first initial attribute data to obtain first target attribute data, including: a third adjustment module, used to adjust the initial velocity to obtain a target velocity, wherein the first target attribute data includes the target velocity; the adjustment unit 4506 is used to adjust the second initial attribute data based on the first target attribute data to obtain second target attribute data, including: a fourth adjustment module, used to adjust the initial size of the particle model based on the target velocity to obtain a target size, wherein the second target attribute data includes the target size.

[0212] Optionally, the fourth adjustment module includes: a separation sub-module, used to separate sub-speeds corresponding to multiple color channels from the target speed; a third adjustment sub-module, used to adjust the initial size using the sub-speeds; and a merging sub-module, used to merge multiple adjusted initial sizes corresponding to the multiple color channels to obtain the target size.

[0213] Optionally, the third adjustment module includes: a fourth adjustment submodule, configured to adjust the initial speed using the random speed to obtain a target speed range; and determine the target speed within the target speed range.

[0214] Optionally, the third adjustment module includes: a third determination submodule, used to determine a tangent at a position where the distance from the particle model is less than a distance threshold on a geometric curve associated with the particle model; and a fifth adjustment submodule, used to adjust the speed direction of the initial speed using the slope of the tangent to obtain a target speed.

[0215] Optionally, the device further includes: a first control unit for controlling the movement of the particle model in response to the distance between the particle model and the geometric curve being within a target distance range; and / or a second control unit for controlling the movement of the particle model using noise of the particle model.

[0216] Optionally, the first determination module includes at least one of the following: a fourth determination submodule, used to determine identification information of the particle model during movement; and / or a fifth determination submodule, used to determine a random value of the particle model during movement, wherein the random value is used to determine the material of the particle model.

[0217] Optionally, the second determination module includes: a sixth determination submodule, used to determine target attribute data required by the particle model; and an adding submodule, used to add the target attribute data to the initial attribute data to obtain an attribute data set.

[0218] Optionally, the rendering unit 4508 includes: a third determination module, used to determine the first target material associated with the first target attribute data and the second target attribute data; a first rendering module, used to dynamically render and display the particle model using the first target material to obtain the performance result of the particle model emitted from the target model.

[0219] Optionally, the rendering unit 4508 includes: an acquisition module for acquiring a second target material of the target model; a second rendering module for dynamically rendering and displaying the particle model using the second target material, the first target attribute data and the second target attribute data to obtain a performance result of the particle model emitted from the target model.

[0220] In this embodiment, at least one particle model emitted from the target model is determined by a first determination unit 4502. A property data set of the particle model is determined by a second determination unit 4504, wherein the property data set includes at least first initial property data and second initial property data that are related to each other. In the process of controlling the movement of the particle model according to the property data set, the adjustment unit 4506 adjusts the first initial property data to obtain first target property data, and adjusts the second initial property data based on the first target property data to obtain second target property data. The rendering unit 4508 uses the first target property data and the second target property data to dynamically render and display the particle model, and obtains the performance result of the particle model emitted from the target model. That is to say, in the process of controlling the movement of the particle model according to the attribute data set, the embodiment of the present disclosure adjusts the first initial attribute data to obtain the first target attribute data, and adjusts the second initial attribute data based on the first target attribute data to obtain the second target attribute data, that is, the linkage control between the first target attribute data and the second target attribute data is realized, and the first target attribute data and the second target attribute data are further used to dynamically render and display the particle model to obtain the performance result of the particle model emitted from the target model, thereby achieving the technical effect of improving the effect of model rendering and solving the technical problem of poor effect of model rendering.

[0221] It should be noted that the above-mentioned units can be implemented by software or hardware. For the latter, it can be implemented in the following ways, but not limited to this: the above-mentioned units are all located in the same processor; or the above-mentioned units are located in different processors in any combination.

[0222] An embodiment of the present disclosure further provides a computer-readable storage medium, in which a computer program is stored, wherein the computer program is configured to execute the steps of any of the above method embodiments when running.

[0223] Optionally, in this embodiment, the computer-readable storage medium may be configured to store a computer program for performing the following steps:

[0224] S1, determining at least one particle model emitted from a target model;

[0225] S2, determining an attribute data set of the particle model, wherein the attribute data set at least includes first initial attribute data and second initial attribute data that are related to each other;

[0226] S3, in the process of controlling the movement of the particle model according to the attribute data set, adjusting the first initial attribute data to obtain first target attribute data, and adjusting the second initial attribute data based on the first target attribute data to obtain second target attribute data;

[0227] S4, using the first target attribute data and the second target attribute data, dynamically rendering and displaying the particle model to obtain a performance result of the particle model emitted from the target model.

[0228] Optionally, in this embodiment, the above-mentioned computer-readable storage medium may include but is not limited to: a USB flash drive, a read-only memory (ROM), a random access memory (RAM), a mobile hard disk, a magnetic disk or an optical disk, and other media that can store computer programs.

[0229] An embodiment of the present disclosure further provides an electronic device, including a memory and a processor, wherein a computer program is stored in the memory, and the processor is configured to run the computer program to execute the steps in any one of the above method embodiments.

[0230] Optionally, the electronic device may further include a transmission device and an input / output device, wherein the transmission device is connected to the processor, and the input / output device is connected to the processor.

[0231] Optionally, in this embodiment, the processor may be configured to perform the following steps through a computer program:

[0232] S1, determining at least one particle model emitted from a target model;

[0233] S2, determining an attribute data set of the particle model, wherein the attribute data set at least includes first initial attribute data and second initial attribute data that are related to each other;

[0234] S3, in the process of controlling the movement of the particle model according to the attribute data set, adjusting the first initial attribute data to obtain first target attribute data, and adjusting the second initial attribute data based on the first target attribute data to obtain second target attribute data;

[0235] S4, using the first target attribute data and the second target attribute data, dynamically rendering and displaying the particle model to obtain a performance result of the particle model emitted from the target model.

[0236] An embodiment of the present disclosure further provides a computer program product, which may include a computer program, wherein the computer program is configured to execute the steps of any of the above method embodiments when running.

[0237] Optionally, in this embodiment, the computer program product may be configured to include a computer program for performing the following steps:

[0238] S1, determining at least one particle model emitted from a target model;

[0239] S2, determining an attribute data set of the particle model, wherein the attribute data set at least includes first initial attribute data and second initial attribute data that are related to each other;

[0240] S3, in the process of controlling the movement of the particle model according to the attribute data set, adjusting the first initial attribute data to obtain first target attribute data, and adjusting the second initial attribute data based on the first target attribute data to obtain second target attribute data;

[0241] S4, using the first target attribute data and the second target attribute data, dynamically rendering and displaying the particle model to obtain a performance result of the particle model emitted from the target model.

[0242] An embodiment of the present disclosure further provides a computer program product, which may include a non-volatile computer-readable storage medium, wherein the non-volatile computer-readable storage medium stores a computer program, and the computer program is configured to execute the steps of any of the above method embodiments when running.

[0243] Optionally, in this embodiment, the computer program product may be configured to include a non-volatile computer-readable storage medium, and the non-volatile computer-readable storage medium may be configured to store a computer program for performing the following steps:

[0244] S1, determining at least one particle model emitted from a target model;

[0245] S2, determining an attribute data set of the particle model, wherein the attribute data set at least includes first initial attribute data and second initial attribute data that are related to each other;

[0246] S3, in the process of controlling the movement of the particle model according to the attribute data set, adjusting the first initial attribute data to obtain first target attribute data, and adjusting the second initial attribute data based on the first target attribute data to obtain second target attribute data;

[0247] S4, using the first target attribute data and the second target attribute data, dynamically rendering and displaying the particle model to obtain a performance result of the particle model emitted from the target model.

[0248] An embodiment of the present disclosure further provides a computer program, which is configured to execute the steps of any of the above method embodiments when running.

[0249] Optionally, in this embodiment, the computer program is configured to perform the following steps:

[0250] S1, determining at least one particle model emitted from a target model;

[0251] S2, determining an attribute data set of the particle model, wherein the attribute data set at least includes first initial attribute data and second initial attribute data that are related to each other;

[0252] S3, in the process of controlling the movement of the particle model according to the attribute data set, adjusting the first initial attribute data to obtain first target attribute data, and adjusting the second initial attribute data based on the first target attribute data to obtain second target attribute data;

[0253] S4, using the first target attribute data and the second target attribute data, dynamically rendering and displaying the particle model to obtain a performance result of the particle model emitted from the target model.

[0254] Optionally, the specific examples in this embodiment may refer to the examples described in the above embodiments and optional implementation modes, and this embodiment will not be described in detail here.

[0255] Fig.46 is a schematic diagram of an electronic device according to an embodiment of the present disclosure. Fig.46 As shown, the electronic device 4600 is merely an example and should not bring any limitation to the functions and scope of use of the embodiments of the present disclosure.

[0256] like Fig.46 As shown, the electronic device 4600 is in the form of a general computing device. The components of the electronic device 4600 may include, but are not limited to: the at least one processor 4610, the at least one memory 4620, a bus 4630 connecting different system components (including the memory 4620 and the processor 4610), and a display 4640.

[0257] The memory 4620 stores program codes, which can be executed by the processor 4610, so that the processor 4610 executes the steps described in the method part of the embodiment of the present disclosure according to various exemplary embodiments of the present disclosure.

[0258] The memory 4620 may include a readable medium in the form of a volatile storage unit, such as a random access memory unit (RAM) 46201 and / or a cache memory unit 46202, and may further include a read-only memory unit (ROM) 46203, and may also include a non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory.

[0259] In some examples, the memory 4620 may also include a program / utility 46204 having a set (at least one) of program modules 46205, such program modules 46205 including but not limited to: an operating system, one or more application programs, other program modules, and program data, each of which or some combination thereof may include the implementation of a network environment. The memory 4620 may further include a memory remotely disposed relative to the processor 4610, and these remote memories may be connected to the electronic device 4600 via a network. Examples of the above-mentioned network include but are not limited to the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0260] Bus 4630 may represent one or more of several types of bus structures, including a memory unit bus or memory unit controller, a peripheral bus, an accelerated graphics port, a local bus of processor 4610, or a bus using any of a variety of bus architectures.

[0261] The display 4640 may be, for example, a touch screen liquid crystal display (LCD) that enables a user to interact with a user interface of the electronic device 4600 .

[0262] Optionally, the electronic device 4600 may also communicate with one or more external devices 4700 (e.g., keyboards, pointing devices, Bluetooth devices, etc.), one or more devices that enable a user to interact with the electronic device 4600, and / or any device that enables the electronic device 4600 to communicate with one or more other computing devices (e.g., routers, modems, etc.). Such communication may be performed through an input / output (I / O) interface 4650. Furthermore, the electronic device 4600 may also communicate with one or more networks (e.g., a local area network (LAN), a wide area network (WAN), and / or a public network, such as the Internet) through a network adapter 4660. Fig.46 As shown, the network adapter 4660 communicates with other modules of the electronic device 4600 via the bus 4630. It should be understood that although Fig.46 Not shown, other hardware and / or software modules may be used in conjunction with the electronic device 4600, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, redundant arrays of independent disks (RAID) systems, tape drives, and data backup storage systems.

[0263] The electronic device 4600 may further include: a keyboard, a cursor control device (such as a mouse), an input / output interface (I / O interface), a network interface, a power supply and / or a camera.

[0264] It can be understood by those skilled in the art that Fig.46 The structure shown is for illustration only and does not limit the structure of the electronic device. Fig.46 More or fewer components as shown, or with Figure 1 The memory 4620 may be used to store computer programs and corresponding data, such as the computer programs and corresponding data corresponding to the information processing method of the model in the embodiment of the present disclosure. The processor 4610 executes various functional applications and data processing by running the computer programs stored in the memory 4620, that is, implements the above-mentioned information processing method of the model.

[0265] The serial numbers of the above-mentioned embodiments of the present disclosure are only for description and do not represent the advantages or disadvantages of the embodiments.

[0266] In the above embodiments of the present disclosure, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0267] In the several embodiments provided in the present disclosure, it should be understood that the disclosed technical content can be implemented in other ways. Among them, the device embodiments described above are only schematic. For example, the division of units can be a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of units or modules, which can be electrical or other forms.

[0268] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed over multiple units. Some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0269] In addition, each functional unit in each embodiment of the present disclosure may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0270] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present disclosure, or the part that contributes to the relevant technology or all or part of the technical solution, can be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, a server or a network device, etc.) to perform all or part of the steps of the various embodiments of the present disclosure. The aforementioned storage medium includes: U disk, read-only memory (ROM), random access memory (RAM), mobile hard disk, disk or optical disk, etc., which can store program code.

[0271] The above are only preferred embodiments of the present disclosure. It should be pointed out that, for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present disclosure. These improvements and modifications should also be regarded as the protection scope of the present disclosure.

Claims

1. A method for processing information of a model, characterized in that: include: determining at least one particle model emitted from the target model; Determining a property data set of the particle model, wherein the property data set includes at least first initial property data and second initial property data that are associated with each other; In the process of controlling the movement of the particle model according to the attribute data set, adjusting the first initial attribute data to obtain first target attribute data, and adjusting the second initial attribute data based on the first target attribute data to obtain second target attribute data; The particle model is dynamically rendered and displayed using the first target attribute data and the second target attribute data to obtain a representation result of the particle model emitted from the target model.

2. The method according to claim 1, characterized in that Determining a property data set of the particle model includes: Determining initial attribute data associated with the motion process of the particle model, wherein the initial attribute data includes the first initial attribute data and the second initial attribute data; Based on the initial attribute data, the attribute data set is determined.

3. The method according to claim 2, characterized in that Determining initial attribute data associated with the motion process of the particle model includes: Determining a control parameter of the particle model, wherein the control parameter is used to control the target model to emit the particle model; Based on the control parameters, the initial attribute data of the particle model is determined.

4. The method according to claim 3, characterized in that The control parameter includes an emission direction of the particle model from the target model, the initial attribute data includes an initial velocity of the particle model, and based on the control parameter, determining the initial attribute data of the particle model includes: In the emission direction, the initial velocity of the particle model is determined.

5. The method according to claim 4, characterized in that The initial attribute data includes the target total existence time of the particle model. Based on the control parameter, the initial attribute data of the particle model is determined, including: The total existence time of the target of the particle model moving in the emission direction is determined.

6. The method according to claim 5, characterized in that Determining the total existence time of the target of the particle model moving in the emission direction includes: Determining an initial total existence time of the particle model moving in the emission direction; Using the random duration, adjusting the initial total existence duration to obtain a target duration range; Within the target duration range, determine the total existence duration of the target.

7. The method according to claim 6, characterized in that The method further comprises: Adjust the distribution results of the particle model corresponding to different total existence durations of the target within the target duration range.

8. The method according to claim 5, characterized in that The first initial attribute data includes the existing duration of the particle model. The first initial attribute data is adjusted to obtain first target attribute data, including: Adjusting the existing duration to obtain the adjusted existing duration, wherein the first target attribute data includes the adjusted existing duration; The second initial attribute data is adjusted based on the first target attribute data to obtain the second target attribute data, including: adjusting the second initial attribute data based on the ratio of the adjusted existing duration to the total target existence duration to obtain the second target attribute data.

9. The method according to claim 8, characterized in that The second initial attribute data includes the initial speed, based on the adjusted ratio of the existing duration to the total existence duration of the target, Adjusting the second initial attribute data to obtain second target attribute data includes: Based on the adjusted ratio of the existing duration to the total target duration, the initial speed is adjusted to obtain a target speed, wherein the second target attribute data includes the target speed.

10. The method according to claim 8, characterized in that The second initial attribute data includes an initial size of the particle model. Based on the adjusted ratio of the existing duration to the total existence duration of the target, the second initial attribute data is adjusted to obtain second target attribute data, including: Based on the adjusted ratio of the existing duration to the total existence duration of the target, the initial size of the particle model is adjusted to obtain the target size, wherein the second target attribute data includes the target size.

11. The method according to claim 5, characterized in that The method further comprises: In response to the existing time length of the particle model being greater than the total existing time length of the target, the particle model is deleted.

12. The method according to claim 4, characterized in that The first initial attribute data includes the initial speed, the second initial attribute data includes the initial size of the particle model, and the first initial attribute data is adjusted to obtain the first target attribute data, including: Adjusting the initial speed to obtain a target speed, wherein the first target attribute data includes the target speed; Adjusting the second initial attribute data based on the first target attribute data to obtain second target attribute data includes: adjusting the initial size of the particle model based on the target speed to obtain a target size, wherein the second target attribute data includes the target size.

13. The method according to claim 12, characterized in that Based on the target speed, the initial size of the particle model is adjusted to obtain a target size, including: Separating sub-speeds corresponding to a plurality of color channels from the target speed; adjusting the initial size using the sub-speed; The multiple adjusted initial sizes corresponding to the multiple color channels are merged to obtain the target size.

14. The method according to claim 12, characterized in that The initial speed is adjusted to obtain a target speed, including: Using random speed, adjusting the initial speed to obtain a target speed range; The target speed is determined within the target speed range.

15. The method according to claim 12, characterized in that The initial speed is adjusted to obtain a target speed, including: On a geometric curve associated with the particle model, determining a tangent line at a position where the distance to the particle model is less than a distance threshold; The speed direction of the initial speed is adjusted using the slope of the tangent line to obtain the target speed.

16. The method according to claim 15, characterized in that The method further comprises: In response to the distance between the particle model and the geometric curve being within a target distance range, controlling the movement of the particle model; and / or The movement of the particle model is controlled by utilizing the noise of the particle model.

17. The method according to claim 2, characterized in that Determining initial attribute data associated with the motion process of the particle model includes at least one of the following: Determining identification information of the particle model during movement; and / or Determine a random value of the particle model during the movement, wherein the random value is used to determine the material of the particle model.

18. The method according to claim 2, characterized in that Determining the attribute data set based on the initial attribute data includes: Determining target attribute data required for the particle model; The target attribute data is added to the initial attribute data to obtain the attribute data set.

19. The method according to any one of claims 1 to 18, characterized in that Using the first target attribute data and the second target attribute data, dynamically rendering and displaying the particle model to obtain a performance result of the particle model emitted from the target model, including: Determining a first target material associated with the first target attribute data and the second target attribute data; The particle model is dynamically rendered and displayed using the first target material to obtain a representation result of the particle model emitted from the target model.

20. The method according to any one of claims 1 to 18, characterized in that Using the first target attribute data and the second target attribute data, dynamically rendering and displaying the particle model to obtain a performance result of the particle model emitted from the target model, including: Acquire a second target material of the target model; The particle model is dynamically rendered and displayed using the second target material, the first target attribute data, and the second target attribute data to obtain a performance result of the particle model emitted from the target model.

21. A model information processing device, characterized in that include: A first determining unit, used to determine at least one particle model emitted from the target model; A second determining unit, configured to determine an attribute data set of the particle model, wherein the attribute data set at least includes first initial attribute data and second initial attribute data that are associated with each other; an adjusting unit, configured to adjust the first initial attribute data to obtain first target attribute data, and to adjust the second initial attribute data based on the first target attribute data to obtain second target attribute data, during the process of controlling the movement of the particle model according to the attribute data set; A rendering unit is used to dynamically render and display the particle model using the first target attribute data and the second target attribute data to obtain a performance result of the particle model emitted from the target model.

22. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, wherein the computer program is configured to execute the method according to any one of claims 1 to 20 when executed by a processor.

23. An electronic device comprising a memory and a processor, characterized in that: A computer program is stored in the memory, and the processor is configured to run the computer program to perform the method according to any one of claims 1 to 20.

24. A computer program product, characterized in that The invention comprises a computer program which, when executed by a processor, implements the method according to any one of claims 1 to 20.

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