Dynamic loading method and device for three-dimensional digital human model of mobile terminal and medium

Abstract

The invention discloses a dynamic loading method and device for a three-dimensional digital human model of a mobile terminal and a medium. The method comprises: acquiring configuration information of a terminal; evaluating a demand index of the three-dimensional digital human model based on the configuration information; normalizing the evaluation result, and aligning with the model precision to obtain the required model precision; presetting a three-dimensional digital human model library, and determining to-be-loaded model precision matched with the required model precision in the three-dimensional digital human model library; and loading the three-dimensional digital human model according to the precision of the model to be loaded. According to the method, the model precision and the surface number of the three-dimensional digital human can be dynamically adjusted according to the actual performance of the terminal equipment, and the equipment performance is fully utilized while the user experience is ensured.

Description

Technical Field

[0001] The present invention relates to the field of virtual reality technology, and more specifically to a method, device and medium for dynamically loading a three-dimensional digital human model of a mobile terminal. Background Art

[0002] In the context of the rapid development of digital human technology, especially with the prosperity of virtual reality (VR), augmented reality (AR) and the gaming industry, the application scenarios of three-dimensional digital human models are becoming increasingly diverse, from entertainment to education, to commercial display and cultural tourism, and their influence is constantly expanding. However, the current mainstream digital human technology still relies on video streaming, which can provide an intuitive visual experience, but the lack of deep interactivity limits the full exploration of the value of digital humans.

[0003] For example, in the field of culture and tourism, digital humans, as carriers of cultural communication, can vividly display the local customs by wearing clothes with local characteristics and cultural connotations. When watching, users often have a strong interest in the stories and design concepts behind these clothes, and even have the intention to buy the same ones. Unfortunately, digital humans based on video streams cannot respond to such user needs because they are essentially passive displays and cannot realize the interactive experience of clicking on specific clothing parts to obtain more information or directly link to the purchase page.

[0004] In order to overcome this limitation, the industry urgently needs to develop 3D digital human technology that can interact with users in all aspects. With the leap in performance of mobile devices, 3D graphics processing capabilities have been significantly improved, which makes it possible to use 3D digital humans on mobile devices. However, the hardware configurations of mobile devices vary greatly. From low-end to high-end, the CPU, GPU performance and memory capacity vary, which leads to the loading speed and rendering quality of the same 3D model on different devices vary greatly. The traditional one-size-fits-all model loading strategy, that is, providing 3D models of the same accuracy to all devices, not only wastes the processing power of high-end devices, but also sacrifices the user experience of low-end devices.

[0005] Therefore, it becomes crucial to develop an intelligent and adaptable method for dynamic loading of 3D models. Summary of the invention

[0006] In view of this, the present invention provides a method, device and medium for dynamically loading a three-dimensional digital human model on a mobile terminal, aiming to solve the adaptability problem of the three-dimensional digital human model in the prior art when applied in mobile terminals, and to meet the performance requirements of mobile devices with different configurations by dynamically adjusting the complexity of the model, thereby promoting the in-depth application of digital human technology in a wider range of fields.

[0007] In order to achieve the above object, the present invention adopts the following technical solution:

[0008] On the one hand, the present invention discloses a method for dynamically loading a three-dimensional digital human model of a mobile terminal, comprising:

[0009] Get the terminal's configuration information;

[0010] Evaluating the demand index of the three-dimensional digital human model based on the configuration information;

[0011] Normalize the evaluation results and align them with the model accuracy to obtain the required model accuracy;

[0012] Preset a 3D digital human model library, and determine the accuracy of the model to be loaded that matches the required model accuracy in the 3D digital human model library;

[0013] Load the 3D digital human model according to the accuracy of the model to be loaded.

[0014] Preferably, the configuration information of the mobile terminal includes CPU performance, GPU performance, memory capacity, storage speed, network bandwidth and screen resolution.

[0015] As a preferred embodiment, the formula for evaluating the demand index of the three-dimensional digital human model is expressed as:

[0016]

[0017] In the formula, W represents the weight coefficient, C represents the CPU performance, G represents the GPU performance, M represents the memory capacity, S represents the storage speed, N represents the network bandwidth, R represents the screen resolution, and the min value and max value corresponding to each configuration information represent the minimum and maximum values ​​within the evaluation range, respectively.

[0018] Preferably, the normalized evaluation result is aligned with the model accuracy according to the following formula;

[0019] F=F min +(F max -F min )×P norm

[0020] In the formula, p norm represents the normalized evaluation result, F min is the minimum value of the accuracy of the 3D digital human model, F max It is the maximum value of the accuracy of the 3D digital human model.

[0021] Preferably, the three-dimensional digital human model library stores the numbers of faces or vertices corresponding to three-dimensional digital human models of different precisions.

[0022] Preferably, in the three-dimensional digital human model library, the absolute value interpolation between the required model accuracy and the accuracy of the model to be loaded is the smallest.

[0023] On the other hand, the present invention discloses a dynamic loading device for a three-dimensional digital human model of a mobile terminal, comprising:

[0024] A model performance evaluation module, used to obtain configuration information of the mobile terminal and evaluate the demand index of the three-dimensional digital human model based on the configuration information;

[0025] The model accuracy determination module is used to normalize the evaluation results and align them with the model accuracy to obtain the required model accuracy;

[0026] The model loading module is used to determine the accuracy of the model to be loaded that matches the required model accuracy in the three-dimensional digital human model library, and load the three-dimensional digital human model according to the accuracy of the model to be loaded.

[0027] Preferably, it also includes a model library construction module for constructing and updating a three-dimensional digital human model library.

[0028] On the other hand, the present invention provides a mobile terminal readable storage medium having computer program instructions stored thereon, wherein the computer readable instructions can be executed by a processor to implement any of the above methods for dynamically loading a three-dimensional digital human model in a mobile terminal.

[0029] It can be seen from the above technical solutions that the present invention discloses a method, device and medium for dynamically loading a three-dimensional digital human model on a mobile terminal, which is used to dynamically adjust the model accuracy and number of faces of the three-dimensional digital human according to the actual performance of the terminal device, ensuring a smooth and high-quality interactive experience on mobile phones of various configurations.

[0030] Specifically, for high-performance devices, the system should automatically load high-precision models and use their powerful graphics processing capabilities to present delicate and realistic digital humans; for low-performance devices, a simplified model should be loaded to ensure basic interactive functions while avoiding lag, thereby achieving wide cross-device compatibility and excellent user experience.

[0031] Compared with the prior art, the present invention can dynamically adjust the accuracy and detail of the loaded three-dimensional model by analyzing the hardware performance indicators of the target device (such as processor speed, memory size, graphics processing capability, etc.) to ensure user experience while making full use of device performance. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying creative work.

[0033] Figure 1 This is a flow chart of the method for dynamically loading a three-dimensional digital human model on a mobile terminal provided by the present invention. DETAILED DESCRIPTION

[0034] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0035] The embodiment of the present invention discloses a method, device and medium for dynamically loading a 3D digital human model on a mobile terminal, which is used to dynamically load 3D digital humans of different precisions and face counts according to the configuration of a mobile phone, so as to be compatible with mobile phones of different configurations to normally interact with 3D digital humans. Specifically, on high-performance devices, a high-precision, high-face model is selected to display a more delicate image; on devices with limited performance, the model precision is automatically reduced to ensure smooth loading and rendering speed.

[0036] The dynamic loading method of the present invention is as follows: Figure 1 As shown, the following steps are included:

[0037] Get the terminal's configuration information;

[0038] Evaluating the demand index of the three-dimensional digital human model based on the configuration information;

[0039] Normalize the evaluation results and align them with the model accuracy to obtain the required model accuracy;

[0040] Preset a 3D digital human model library, and determine the accuracy of the model to be loaded that matches the required model accuracy in the 3D digital human model library;

[0041] Load the 3D digital human model according to the accuracy of the model to be loaded.

[0042] In an exemplary embodiment,

[0043] S1. Obtain terminal configuration information;

[0044] When the application is started, the detailed configuration information of the mobile device running the application is obtained, including but not limited to the CPU model, GPU model, RAM capacity, storage speed, network status, screen resolution, etc.

[0045] S2. Evaluate the demand index of the three-dimensional digital human model based on the configuration information;

[0046] In this embodiment, a model demand index evaluation model is established based on the computing power, memory, storage, network and other parameters of the device. The model demand index comprehensively reflects the ability of the device to process three-dimensional models. In the model demand index evaluation model, a certain weight is given to the performance indicators of the device, and the weight reflects the importance of each indicator for loading and rendering the three-dimensional model.

[0047] Specifically, the formula for evaluating the demand index of the three-dimensional digital human model is expressed as:

[0048]

[0049] In the formula, W represents the weight coefficient, C represents the CPU performance, G represents the GPU performance, M represents the memory capacity, S represents the storage speed, N represents the network bandwidth, R represents the screen resolution, and the min value and max value corresponding to each configuration information represent the minimum and maximum values ​​within the evaluation range, respectively.

[0050] In this embodiment, R is the resolution of the screen, which is usually expressed as the total number of pixels (i.e., the product of width and height). The higher the resolution, the lower its contribution value. This is because the higher the resolution, the greater the pressure on the GPU, so a certain penalty needs to be given when calculating the performance index. This penalty mechanism assumes that high resolution will bring additional graphics processing burden, so when evaluating overall performance, the impact of resolution will decrease as its value increases.

[0051] It should be noted that the weight distribution needs to be adjusted according to the specific application scenario and target user group. For example, in gaming applications, the weight of GPU and resolution may be higher, while in data processing applications, the weight of CPU and memory may be more important. In addition, the max and min values ​​of Rmax and other parameters also need to be set according to the actual situation of the device.

[0052] In one embodiment, the evaluation model index system table is as follows:

[0053] parameter symbol describe Weight (W) CPU Performance C Expressed as a benchmark score, such as Geekbench single-core or multi-core scores 0.25 GPU Performance G Expressed as a graphics rendering benchmark score, such as GFXBench frame rate 0.25 Memory capacity M In GB, taking into account total available RAM 0.1 Storage speed S Average read and write speeds in MB / s 0.1 Network bandwidth N Measured in Mbps, taking into account the average of upload and download speeds 0.1 Screen resolution R Expressed in pixels (width x height), which can be converted to total pixels 0.2

[0054] S3. Normalize the evaluation results.

[0055] The performance evaluation index P of this application has been normalized to the interval of 0-1 in the evaluation formula. If P is not within this interval, it is normalized again by the following formula to fall within a preset range. The normalization formula is:

[0056]

[0057] Among them, P min and P maxare the minimum and maximum values ​​of the performance evaluation index, respectively.

[0058] S4, aligning with the model accuracy to obtain the required model accuracy;

[0059] In this step, we first define the range of model accuracy, including the number of faces or vertices of the model. min and F max are the minimum and maximum values ​​of the model accuracy, respectively.

[0060] Then, the normalized performance evaluation index P norm Mapping to the model accuracy range [F min ,F max ] The mapping formula is as follows:

[0061] F=F min +(F max -F min )×P norm

[0062] Where F is the required model accuracy calculated based on equipment performance, p norm represents the normalized evaluation result, F min is the minimum value of the accuracy of the 3D digital human model, F max It is the maximum value of the accuracy of the 3D digital human model.

[0063] S5. Preset 3D digital human model library.

[0064] In this embodiment, a 3D digital human model library is pre-built. The model library includes a series of preset model accuracy levels. Each level corresponds to a specific number of faces or vertices, which is expressed as: {F1, F2, ..., F n}, where n is the number of preset models.

[0065] S6. Determine the accuracy of the model to be loaded that matches the required model accuracy in the three-dimensional digital human model library; and load the three-dimensional digital human model according to the accuracy of the model to be loaded.

[0066] In a preferred embodiment, this is achieved by calculating the absolute value of the difference between the preset model accuracy and F, and selecting the model with the smallest difference. The calculation formula includes:

[0067] Δi=|FF i |,i=1,2,3,...,n

[0068] Further, select the F with the smallest Δi i The corresponding model is used as the most suitable model accuracy; and according to the determined model accuracy level, the 3D digital human model with the corresponding face number accuracy is dynamically downloaded from the server. The server pre-stores models of multiple resolutions to meet the needs of different devices.

[0069] In a specific embodiment, given the value of P, assuming P min =0,P max =1,F min =10000 faces,F max = 1000000 faces; loading steps are:

[0070] 1. Normalized performance evaluation index:

[0071] P_{norm}=P

[0072] Since P is already between 0 and 1, use P directly.

[0073] 2. Mapping to model accuracy range:

[0074] F=10000+(1000000-10000)*P_{norm}

[0075] 3. Choose the most suitable model:

[0076] Assume that the preset model accuracy is {10000, 50000, 100000, 500000, 1000000}, calculate the absolute value of the difference between each preset model accuracy and F, and select the model corresponding to the minimum value.

[0077] Through the above steps, the application can select the best model accuracy based on the performance evaluation index P of the device, thereby ensuring that the application performs smoothly and has high-quality visual effects on different devices.

[0078] That is, the three-dimensional digital human model dynamic loading method applied to the terminal provided in this embodiment can automatically match the most appropriate model accuracy on devices with different configurations, thereby maximizing the use of device resources while ensuring user experience.

[0079] In another embodiment, the present invention provides a mobile terminal three-dimensional digital human model dynamic loading device, and a mobile terminal readable storage medium;

[0080] The device comprises:

[0081] A model performance evaluation module, used to obtain configuration information of the mobile terminal and evaluate the demand index of the three-dimensional digital human model based on the configuration information;

[0082] The model accuracy determination module is used to normalize the evaluation results and align them with the model accuracy to obtain the required model accuracy;

[0083] Model library construction module, used to build and update the 3D digital human model library;

[0084] The model loading module is used to determine the accuracy of the model to be loaded that matches the required model accuracy in the three-dimensional digital human model library, and load the three-dimensional digital human model according to the accuracy of the model to be loaded.

[0085] The readable storage medium stores computer program instructions, and the computer readable instructions can be executed by a processor to implement the above-mentioned method for dynamically loading a three-dimensional digital human model in a mobile terminal.

[0086] Aiming at the application scenarios of digital humans, the present invention proposes a device performance evaluation model with adjustable weights, and designs a mapping calculation method between device performance and digital human accuracy; it solves the difficult problems of cross-device model compatibility and performance optimization, and has broad application prospects.

[0087] Compared with the prior art, the beneficial effects of this application include:

[0088] (1) Resource optimization: By dynamically matching model accuracy, we can avoid the problem of resource waste on high-end devices and loading failure on low-end devices.

[0089] (2) Improved user experience: This ensures smooth loading and high-quality rendering of the model on any device, improving the overall user experience.

[0090] (3) Flexibility: Suitable for various types of mobile devices, with high adaptability and scalability.

[0091] In this specification, each embodiment is described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the embodiments can be referred to each other. For the device disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant parts can be referred to the method part.

[0092] The above description of the disclosed embodiments enables one skilled in the art to implement or use the present invention. Various modifications to these embodiments will be apparent to one skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but rather to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for dynamically loading a three-dimensional digital human model on a mobile terminal, characterized in that: include: Get the terminal's configuration information; Evaluating the demand index of the three-dimensional digital human model based on the configuration information; Normalize the evaluation results and align them with the model accuracy to obtain the required model accuracy; Preset a 3D digital human model library, and determine the accuracy of the model to be loaded that matches the required model accuracy in the 3D digital human model library; Load the 3D digital human model according to the accuracy of the model to be loaded.

2. A method for dynamically loading a three-dimensional digital human model on a mobile terminal according to claim 1, characterized in that: The configuration information of the mobile terminal includes CPU performance, GPU performance, memory capacity, storage speed, network bandwidth and screen resolution.

3. A method for dynamically loading a three-dimensional digital human model on a mobile terminal according to claim 1, characterized in that: The formula for evaluating the demand index of the three-dimensional digital human model is expressed as: In the formula, W represents the weight coefficient, C represents the CPU performance, G represents the GPU performance, M represents the memory capacity, S represents the storage speed, N represents the network bandwidth, R represents the screen resolution, and the min value and max value corresponding to each configuration information represent the minimum and maximum values ​​within the evaluation range, respectively.

4. A method for dynamically loading a three-dimensional digital human model on a mobile terminal according to claim 1, characterized in that: The normalized evaluation results are aligned with the model accuracy according to the following formula; F=F min +(F max -F min )×P norm In the formula, p norm represents the normalized evaluation result, F min is the minimum value of the accuracy of the 3D digital human model, F max It is the maximum value of the accuracy of the 3D digital human model.

5. The method for dynamically loading a three-dimensional digital human model on a mobile terminal according to claim 1, characterized in that: The three-dimensional digital human model library stores the numbers of faces or vertices corresponding to three-dimensional digital human models of different precisions.

6. A method for dynamically loading a three-dimensional digital human model on a mobile terminal according to claim 1, characterized in that: In the three-dimensional digital human model library, the absolute value interpolation between the required model accuracy and the accuracy of the model to be loaded is the smallest.

7. A dynamic loading device for a three-dimensional digital human model on a mobile terminal, characterized in that: include: A model performance evaluation module, used to obtain configuration information of the mobile terminal and evaluate the demand index of the three-dimensional digital human model based on the configuration information; The model accuracy determination module is used to normalize the evaluation results and align them with the model accuracy to obtain the required model accuracy; The model loading module is used to determine the accuracy of the model to be loaded that matches the required model accuracy in the three-dimensional digital human model library, and load the three-dimensional digital human model according to the accuracy of the model to be loaded.

8. The mobile terminal three-dimensional digital human model dynamic loading device according to claim 7, characterized in that: It also includes a model library building module for building and updating a three-dimensional digital human model library.

9. A mobile terminal readable storage medium having computer program instructions stored thereon, characterized in that: The computer program instructions can be executed by a processor to implement a method for dynamically loading a three-dimensional digital human model on a mobile terminal as described in any one of claims 1-6.

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