Animation Production Systems and Methods

Abstract

This application provides an animation production system and method. It identifies interactive semantic regions in static background images within initial animation production materials that interact with the character. Based on the character's motion trajectory and the geometric parameters of the interactive semantic regions in the character's animation sequence, it detects potential contact points between the character and the interactive semantic regions. When a potential contact point is detected, it simulates the deformation effect of the character's actions on the interactive semantic regions based on preset physical attribute parameters of the interactive semantic regions, generating a corresponding background deformation field. It identifies the synthetic texture of the background region exposed by the deformation based on the texture features of the interactive semantic regions, and generates a confident background sequence that evolves synchronously with the character's actions from the background deformation field and the synthetic texture. Based on the character animation sequence and the confident background sequence, it outputs the target animation. The technical solution provided by this application enables reliable dynamic interaction between the character and the background environment during animation production.

Description

Technical Field

[0001] This application relates to the field of animation production technology, and more specifically, to an animation production system and method. Background Technology

[0002] Modern animation production has deeply integrated computer technology, with 3D animation being the mainstream. Its core process includes determining the visual style in the early stages using concept art and storyboards, building characters and scenes through modeling in the middle stages, binding skeletons to give models the ability to move, and then animators creating performances using keyframe technology or motion capture systems. Next, materials and lighting are set, and the rendering engine calculates and generates the final image. 2D animation relies more on digital hand-drawing and vector graphics technology, supplemented by skeletal tools to improve efficiency. In addition, physical simulation technology is widely used to handle complex dynamics such as hair and cloth. The rise of real-time rendering engines is revolutionizing the production cycle and driving the animation industry to iterate towards higher efficiency and stronger visual expression.

[0003] In current animation production, the main approach is based on the phenomenon of "persistence of vision," creating the illusion of motion by rapidly playing static images in succession. Specifically, the production process is divided into two main paths: 2D and 3D. 2D animation continues the principle of "frame-by-frame rendering," efficiently generating a sequence of frames by manipulating a digital skeleton system. 3D animation follows the principle of "virtual production," first creating digital models of characters and scenes in a computer, adding skeletons and controllers, and then automatically outputting dynamic images. However, in the traditional 2D animation production process based on static background images and character animation sequences, the complete separation of background and characters during the production stage results in a lack of visually believable dynamic interaction between characters and the background environment based on physical rules. This leads to a loss of realism in the animated scenes. Therefore, how to achieve believable dynamic interaction between characters and the background environment during animation production has become a challenge for the industry. Summary of the Invention

[0004] This application provides an animation production system and method that enables believable dynamic interaction between characters and the background environment during the animation production process.

[0005] In a first aspect, this application provides an animation production method, comprising the following steps:

[0006] Obtain initial animation assets containing character animation sequences and static background images;

[0007] Semantic segmentation is performed on the static background image in the initial animation production material to identify the interactive semantic region in the static background image that interacts with the character. Based on the character's motion trajectory in the character animation sequence and the geometric parameters of the interactive semantic region, potential contact points between the character and the interactive semantic region are detected.

[0008] When a potential contact point is detected, the deformation effect of the character's actions on the interactive semantic region is simulated based on the preset physical attribute parameters of the interactive semantic region, and a corresponding background deformation field is generated.

[0009] Based on the texture features of the interactive semantic region, the background region exposed due to deformation is textured to obtain the synthesized texture of the background region. Then, the background deformation field and the synthesized texture are applied to the static background image to generate a confidence background sequence that evolves synchronously with the character's movements.

[0010] The target animation is output based on the character animation sequence and the confidence background sequence.

[0011] In some embodiments, semantic segmentation is performed on the static background image in the initial animation production material to identify the interactive semantic regions in the static background image that interact with the character, specifically including:

[0012] The static background image is preprocessed to obtain a preprocessed background image;

[0013] A pre-trained semantic segmentation model is used to perform pixel-level classification on the preprocessed background image, thereby obtaining segmentation results containing various semantic labels;

[0014] Candidate semantic regions with deformable characteristics are selected from the segmentation results;

[0015] Obtain the character animation sequence from the initial animation production materials, and define the interaction detection area based on the movement range of the character animation sequence;

[0016] Within the interaction detection area, interactive semantic regions with potential interactive relationships with the character are determined from candidate semantic regions with deformable characteristics.

[0017] In some embodiments, detecting potential contact points between the character and the interactive semantic region based on the character's motion trajectory in the character animation sequence and the geometric parameters of the interactive semantic region specifically includes:

[0018] Extract the motion trajectory data of the character in the character animation sequence and the geometric parameters of the interactive semantic region;

[0019] Based on the motion trajectory data and the geometric parameters, a spatial coordinate system is constructed between the character and the interactive semantic region;

[0020] Calculate the spatial distance between the character's motion trajectory and the interactive semantic region in the spatial coordinate system;

[0021] The spatial distance is compared with a preset contact threshold to filter out trajectory points that meet the contact conditions, and these trajectory points that meet the contact conditions are identified as potential contact points between the character and the interactive semantic area.

[0022] In some embodiments, the process of synthesizing a texture of the background region exposed due to deformation based on the texture features of the interactive semantic region to obtain the synthesized texture of the background region specifically includes:

[0023] The pixel coordinates of the background area exposed due to deformation are determined based on the dynamic background deformation field.

[0024] Extract texture feature parameters around the interactive semantic region, and then construct a texture feature library;

[0025] Extract effective texture samples of the background region based on the pixel coordinates of the exposed background region;

[0026] The texture feature library is used to perform feature matching on valid texture samples to generate a synthetic texture for the background region.

[0027] In some embodiments, applying the background deformation field and the synthesized texture to the static background image to generate a confident background sequence that evolves synchronously with the character's movements specifically includes:

[0028] Based on the background deformation field, geometric deformation transformation is performed on the interactive semantic region in the static background image to generate a preliminary deformed background frame.

[0029] In the initial deformed background frame, locate the background area exposed by the deformation, and map the synthetic texture to the background area to complete pixel supplementation;

[0030] The edge blending process is performed on the supplemented preliminary deformed background frame to obtain the blended deformed background frame.

[0031] Based on the timeline parameters of the character animation sequence, the fused deformed background frames are arranged in chronological order, and inter-frame interpolation optimization is performed on the chronologically arranged deformed background frames to generate a confidence background sequence that evolves synchronously with the character's movements.

[0032] In some embodiments, outputting the target animation based on the character animation sequence and the confidence background sequence specifically includes:

[0033] Extract the timestamp information of the character animation sequence and the confidence background sequence;

[0034] Based on the timestamp information, each character frame of the character animation sequence is time-aligned with the background frame corresponding to the confidence background sequence.

[0035] Perform pixel-level fusion on the time-aligned character frames and background frames to generate dynamic composite frames with characters;

[0036] Arrange all dynamic composite frames in chronological order to output the target animation.

[0037] In some embodiments, initial animation materials containing character animation sequences and static background images are obtained through an animation production data storage device.

[0038] In some embodiments, the character animation sequence consists of multiple character frames.

[0039] In some embodiments, the initial animation production material is obtained by combining a scale-aligned sequence of character animations and a static background image.

[0040] Secondly, this application provides an animation production system, which includes:

[0041] The acquisition module is used to acquire initial animation materials, including character animation sequences and static background images;

[0042] The processing module is used to perform semantic segmentation on the static background image in the initial animation production material, identify the interactive semantic region in the static background image that interacts with the character, and detect the potential contact point between the character and the interactive semantic region based on the character's motion trajectory in the character animation sequence and the geometric parameters of the interactive semantic region.

[0043] The processing module is also used to simulate the deformation effect of the character's actions on the interactive semantic region based on the preset physical attribute parameters of the interactive semantic region when a potential contact point is detected, and generate a corresponding background deformation field.

[0044] The processing module is further configured to synthesize the texture of the background area exposed due to deformation based on the texture features of the interactive semantic region, to obtain the synthesized texture of the background area, and then apply the background deformation field and the synthesized texture to the static background image to generate a confidence background sequence that evolves synchronously with the character's movements.

[0045] The execution module is used to output the target animation based on the character animation sequence and the confidence background sequence.

[0046] Thirdly, this application provides a computer device, the computer device including a memory and a processor, the memory storing code, and the processor being configured to acquire the code and execute the above-described animation production method.

[0047] Fourthly, this application provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described animation production method.

[0048] The technical solutions provided by the embodiments disclosed in this application have the following beneficial effects:

[0049] The animation production system and method provided in this application firstly acquire initial animation production materials including a character animation sequence and a static background image; secondly, semantic segmentation is performed on the static background image in the initial animation production materials to identify interactive semantic regions in the static background image that interact with the character, and potential contact points between the character and the interactive semantic regions are detected based on the character's motion trajectory in the character animation sequence and the geometric parameters of the interactive semantic regions; further, when potential contact points are detected, the deformation effect of the character's actions on the interactive semantic regions is simulated based on preset physical attribute parameters of the interactive semantic regions to generate a corresponding background deformation field; then, the background region exposed due to deformation is textured according to the texture features of the interactive semantic regions to obtain a synthesized texture of the background region, and then the background deformation field and the synthesized texture are applied to the static background image to generate a confidence background sequence that evolves synchronously with the character's actions; finally, the target animation is output based on the character animation sequence and the confidence background sequence.

[0050] Therefore, this application achieves reliable dynamic interaction between characters and the background environment during animation production. First, it acquires initial animation materials containing character animation sequences and static background images, providing complete basic data support for subsequent animation production. This ensures the integrity of character dynamic information and background static information, avoiding deviations in subsequent interaction simulation or deformation calculations due to missing materials. Second, it performs semantic segmentation on the static background image to identify interactive semantic regions and detects potential contact points based on the character's motion trajectory. This accurately locates the interaction objects and specific action positions between the character and the background, avoiding wasted computational power due to indiscriminate processing, and provides precise target points for subsequent deformation simulation, ensuring the rationality and targeting of background deformation. Furthermore, upon detecting potential contact points, it generates a background deformation field based on the physical properties of the interactive semantic regions. The impact of character actions on the background can be transformed into pixel-level displacement commands, making background deformation conform to real physical laws and avoiding the lack of visually believable dynamic interaction between the character and the background environment based on physical rules, thereby improving the physical realism of the animation scene. Then, based on the texture features of the interactive semantic region, the exposed region texture is synthesized and combined with the background deformation field to generate a confident background sequence. This can not only fill the background blank caused by deformation and ensure texture continuity, but also synchronize the background and character action sequence, eliminating the visual disconnect of "static background + dynamic character". Finally, based on the character animation sequence and the confident background sequence, the target animation is output to achieve deep integration of the character and the background, improving the efficiency and quality of animation production. In summary, the technical solution provided in this application can realize believable dynamic interaction between the character and the background environment during the animation production process. Attached Figure Description

[0051] Figure 1 This is a schematic diagram of an application scenario architecture for an animation production method according to some embodiments of this application;

[0052] Figure 2 This is an exemplary flowchart of an animation production method according to some embodiments of this application;

[0053] Figure 3 This is an exemplary flowchart illustrating the determination of potential contact points according to some embodiments of this application;

[0054] Figure 4 This is a schematic diagram of the structure of an animation production system according to some embodiments of this application;

[0055] Figure 5 This is a schematic diagram of the structure of a computer device for implementing an animation production method according to some embodiments of this application. Detailed Implementation

[0056] To better understand the technical solution of this application, the technical solution of this application will be described in detail below with reference to the accompanying drawings and specific embodiments.

[0057] refer to Figure 1 This figure is a schematic diagram of an application scenario architecture for an animation production method according to some embodiments of this application. The application scenario architecture includes a data acquisition terminal, a communication network, and a server. The data acquisition terminal and the server are directly or indirectly connected through the communication network. The data acquisition terminal acquires initial animation production materials containing a character animation sequence and a static background image and uploads them to the server. The server performs semantic segmentation on the static background image in the initial animation production materials, identifies interactive semantic regions in the static background image that interact with the character, and detects potential contact points between the character and the interactive semantic regions based on the character's motion trajectory in the character animation sequence and the geometric parameters of the interactive semantic regions. When a potential contact point is detected, the deformation effect of the character's actions on the interactive semantic regions is simulated based on preset physical attribute parameters of the interactive semantic regions, generating a corresponding background deformation field. Texture synthesis is performed on the background regions exposed due to deformation based on the texture features of the interactive semantic regions to obtain a synthesized texture of the background regions. The background deformation field and the synthesized texture are then applied to the static background image to generate a confident background sequence that evolves synchronously with the character's actions. The target animation is output based on the character animation sequence and the confident background sequence.

[0058] refer to Figure 2 The figure is an exemplary flowchart of an animation production method according to some embodiments of this application. The animation production method mainly includes the following steps:

[0059] In step 101, initial animation production materials containing character animation sequences and static background images are obtained, wherein the character animation sequence consists of multiple keyframes.

[0060] In practice, initial animation materials containing character animation sequences and static background images can be obtained through an animation production data storage device. The character animation sequence refers to a dynamic data set that describes the posture, position, and shape changes of a virtual character within its movement cycle through a series of character frames arranged in chronological order. The character animation sequence consists of character frames pre-set by the animator. These character frames define the precise posture of the character at key moments in the movement (such as starting, highest point, and landing). The character animation sequence consists of multiple character frames. The static background image refers to a two-dimensional digital image that does not contain preset dynamic change elements in the time dimension of the animation. The static background image is specifically pre-drawn by the animator, which will not be elaborated here.

[0061] It should be noted that the initial animation production material in this application refers to the initial animation production material containing character animation sequences and static background images. The initial animation production material is obtained by combining the scale-aligned character animation sequences and static background images. The scale alignment means that the character frames in the character animation sequence and the static background images are of the same size. By obtaining the initial animation production material, a data foundation can be provided for the subsequent target animation production.

[0062] In step 102, semantic segmentation is performed on the static background image in the initial animation production material to identify the interactive semantic region in the static background image that interacts with the character. Based on the character's motion trajectory in the character animation sequence and the geometric parameters of the interactive semantic region, potential contact points between the character and the interactive semantic region are detected.

[0063] In some embodiments, semantic segmentation of the static background image in the initial animation production material to identify the interactive semantic regions in the static background image that interact with the character is specifically achieved through the following steps:

[0064] The static background image is preprocessed to obtain a preprocessed background image;

[0065] A pre-trained semantic segmentation model is used to perform pixel-level classification on the preprocessed background image, thereby obtaining segmentation results containing various semantic labels;

[0066] Candidate semantic regions with deformable characteristics are selected from the segmentation results;

[0067] Obtain the character animation sequence from the initial animation production materials, and define the interaction detection area based on the movement range of the character animation sequence;

[0068] Within the interaction detection area, interactive semantic regions with potential interactive relationships with the character are determined from candidate semantic regions with deformable characteristics.

[0069] In specific implementation, firstly, Gaussian filtering is used to denoise the static background image, and the denoised static background image is used as the preprocessed background image. The preprocessed background image refers to the static background image after denoising. Secondly, the preprocessed background image is input into the encoder part of a pre-trained semantic segmentation model (such as a U-shaped network model). The encoder extracts low-level features (such as edges and textures) and high-level features (such as semantic category information) from the preprocessed background image through convolutional and pooling layers. Then, the decoder part fuses the high-level features with the corresponding low-level features through upsampling and skip connections, reconstructing a pixel-level classification map with the same size as the preprocessed background image and containing various semantic labels, thereby obtaining the semantic labels. The segmentation result refers to an image where each pixel is assigned a corresponding semantic category label (such as ground, trees, cloth, etc.). The pre-trained semantic segmentation model is trained using a labeled dataset constructed from the target task (such as natural scene segmentation). The labeled dataset contains a large number of original images and pixel-level semantic annotations for each image. Further, based on a pre-defined deformable semantic category library (such as a set of known deformable object categories like cloth, hair, and water), the semantic labels of each pixel in the segmentation result are traversed. Pixels whose semantic labels belong to the deformable semantic category library are subjected to connected component analysis (i.e., using a region growing algorithm, taking any deformable category pixel as a seed point, and gradually connecting pixels of the same category in the neighborhood that satisfy gray-level similarity). Pixels with a threshold value are merged to form continuous regions, and the obtained regions are used as candidate semantic regions. Candidate semantic regions with deformable characteristics are obtained, where the candidate semantic region refers to an independent image region whose semantic category belongs to the deformable category, selected from the segmentation results. Then, the character animation sequence from the initial animation production material is obtained, and the bounding rectangle of each character frame in the character animation sequence is extracted frame by frame. The maximum x-coordinate, minimum x-coordinate, maximum y-coordinate, and minimum y-coordinate of the bounding rectangle of each character frame are obtained. The region formed by connecting the maximum x-coordinate, minimum x-coordinate, maximum y-coordinate, and minimum y-coordinate is taken as the motion range of the character animation sequence, and the motion range is used as the intersection point. The mutual detection region refers to a rectangular image region used to filter potential interaction regions. The interaction detection region includes candidate semantic regions with deformable characteristics. Finally, within the interaction detection region, interactive semantic regions with potential interaction relationships with the character are determined from the candidate semantic regions with deformable characteristics. That is, the intersection area of ​​each candidate semantic region with deformable characteristics and the interaction detection region is calculated. If the proportion of the intersection area to the total area of ​​the candidate semantic regions exceeds a preset threshold, it is determined that the candidate semantic region has a potential interaction relationship with the character, and the candidate semantic region is determined as an interactive semantic region. The preset threshold can be set according to actual needs, for example, it can be set to 50%, which is not limited here.

[0070] It should be noted that, in this application, the interactive semantic region refers to a semantic region within the interaction detection area that has a potential interactive relationship with the character and possesses deformable characteristics. By determining the interactive semantic region, specific areas in the static background image that may interact with the character's actions (such as deformable areas like cloth, water, and hair) can be accurately selected. This avoids indiscriminate processing of the entire background image in subsequent animation production processes, significantly reducing computational resource consumption and processing time. At the same time, it clarifies the background objects that the character's actions need to be associated with, providing accurate regional positioning basis for subsequent simulation of background deformation based on physical properties (such as wrinkles when the character touches the cloth or ripples when stepping on water). This ensures that background deformation only occurs within a reasonable interaction range, avoids abnormal background deformation unrelated to the character's actions, and guarantees the physical logic of the animation scene.

[0071] In some embodiments, reference Figure 3 As shown, this figure is an exemplary flowchart illustrating the determination of potential contact points according to some embodiments of this application. In this embodiment, detecting potential contact points between a character and an interactive semantic region based on the character's motion trajectory in the character animation sequence and the geometric parameters of the interactive semantic region can be achieved using the following steps:

[0072] First, in step 1021, the motion trajectory data of the character in the character animation sequence and the geometric parameters of the interactive semantic region are extracted;

[0073] Secondly, in step 1022, a spatial coordinate system between the character and the interactive semantic region is constructed based on the motion trajectory data and the geometric parameters;

[0074] Then, in step 1023, the spatial distance between the character's motion trajectory and the interactive semantic region is calculated in the spatial coordinate system;

[0075] Finally, in step 1024, the spatial distance is compared with the preset contact threshold to filter out the trajectory points that meet the contact conditions, and the trajectory points that meet the contact conditions are determined as potential contact points between the character and the interactive semantic area.

[0076] In specific implementation, firstly, the pixel coordinates of key skeletal points (such as head, hands, and feet) of the character are obtained frame by frame from the character animation sequence. The pixel coordinates of the key skeletal points in each frame are then concatenated in chronological order to form motion trajectory data. The contour of the interactive semantic region is obtained through a contour detection algorithm, and the minimum bounding rectangle and center point coordinates of the contour are calculated to obtain the geometric parameters of the interactive semantic region. The motion trajectory data refers to the coordinate sequence formed by concatenating the coordinates of the key skeletal points of the character in chronological order. The geometric parameters refer to the set of the minimum bounding rectangle and center point coordinates of the interactive semantic region. Secondly, a coordinate system is established with the lower left corner of the static background image as the origin, the horizontal direction to the right as the positive x-axis, and the vertical direction upward as the positive y-axis. The coordinates of the key skeletal points in the motion trajectory data and the coordinates contained in the minimum bounding rectangle and the center point coordinates in the geometric parameters of the interactive semantic region are transformed into the coordinate system. This constructs a spatial coordinate system between the character and the interactive semantic region. The spatial coordinate system refers to a two-dimensional planar coordinate system constructed based on the static background image. Then, in the spatial coordinate system, the shortest vertical distance from the coordinates of each key bone point on the motion trajectory to the smallest bounding rectangle of the interactive semantic region is calculated, thereby obtaining the spatial distance between the character's motion trajectory and the interactive semantic region. The spatial distance refers to the shortest distance from each key bone point on the motion trajectory to the outline of the interactive semantic region. Finally, the spatial distance is compared with a preset contact threshold to filter out trajectory points that meet the contact conditions, and these trajectory points are identified as potential contact points between the character and the interactive semantic region. That is, it is determined whether the spatial distance of each trajectory point is less than or equal to the preset contact threshold. If it is, the trajectory point is considered to meet the contact conditions; otherwise, it is considered to not meet the contact conditions. The trajectory points that meet the contact conditions are identified as potential contact points. The preset contact threshold can be set according to the accuracy requirements of the animation scene and is not limited here. The preset contact threshold is used to determine whether the trajectory point is close to the distance threshold of the interactive semantic region. The trajectory point that meets the contact conditions refers to the trajectory point whose spatial distance is less than or equal to the preset contact threshold.

[0077] It should be noted that, in this application, a potential contact point refers to a trajectory point that is determined to be likely to come into contact with an interactive semantic region. By determining the potential contact point, the specific spatial location and time node where the character and the interactive semantic region may interact in the character animation sequence can be accurately located. This provides a core target point for simulating the deformation effect of the character's actions on the background, avoiding indiscriminate or misaligned physical feedback in the deformation simulation due to the lack of a clear action location. In addition, a clear potential contact point can also provide a precise time anchor point for the synchronization of the background sequence and the character animation sequence, ensuring that the start, change and recovery process of the background deformation strictly matches the action sequence of the character's contact, action and detachment, avoiding visual discontinuities where the background deformation and the character's actions are out of sync, and ultimately ensuring the realism of the interaction process between the character and the background and the continuity of the animation scene.

[0078] In step 103, when a potential contact point is detected, the deformation effect of the character's actions on the interactive semantic region is simulated based on the preset physical attribute parameters of the interactive semantic region, and a corresponding background deformation field is generated.

[0079] In some embodiments, when a potential contact point is detected, the deformation effect of a character's actions on the interactive semantic region is simulated based on preset physical attribute parameters of the interactive semantic region, and a corresponding background deformation field is generated. This is specifically achieved through the following steps:

[0080] When a potential contact point is detected, the preset physical attribute parameters of the interactive semantic region and the position and force information of the potential contact point are obtained.

[0081] Construct a finite element model of the interactive semantic region based on the physical property parameters;

[0082] The location and force information of potential contact points are input into the finite element model as boundary conditions, and then the deformation displacement field of the interactive semantic region under the force is calculated through the finite element model.

[0083] A background deformation field corresponding to the interactive semantic region is generated based on the deformation displacement field.

[0084] In specific implementation, firstly, when a potential contact point is detected, the preset physical attribute parameters of the interactive semantic region and the position and force information of the potential contact point are acquired. The preset physical attribute parameters of the interactive semantic region are determined according to its semantic category (e.g., elastic modulus, Poisson's ratio, and density for cloth, viscosity and surface tension coefficient for water). The position of the potential contact point is obtained from the aforementioned spatial coordinate system. The force information of the potential contact point can be set according to the momentum theorem based on the movement speed of the potential contact point and the mass of the character model; this will not be elaborated further here. The physical attribute parameters refer to a set of parameters pre-set according to the category of the interactive semantic region, describing its physical characteristics. The position and force information of the potential contact point... The information refers to a set of information including the spatial coordinates of potential contact points and the magnitude of the force exerted on those points by the action. Secondly, based on the physical property parameters, a finite element model of the interactive semantic region is constructed. This involves meshing the interactive semantic region to obtain different mesh elements. The meshing can use existing quadrilateral or triangular element methods, which are not limited here. Physical property parameters are assigned to the corresponding mesh elements (e.g., elastic modulus and Poisson's ratio are assigned to the mesh elements of the fabric region, and viscosity is assigned to the mesh elements of the water region). Simultaneously, the connection relationships and constraints between mesh elements are defined (e.g., tensile constraints between fabric mesh elements and flow constraints between water mesh elements), thus completing the finite element model of the interactive semantic region. The meta-model construction involves constructing a finite element model (FEM), which discretizes the interactive semantic region into multiple mesh elements and assigns physical properties and constraints to each element. The location and force information of potential contact points are then input into the FEM as boundary conditions. The FEM then calculates the deformation and displacement field of the interactive semantic region under applied forces. The process begins by locating the corresponding mesh element (i.e., contact element) in the FEM based on the location information of the potential contact points. The force information is then decomposed into components along the x and y axes of the contact element in the spatial coordinate system and applied as loads to the mesh element. Finally, a dynamic solution algorithm (such as Newton's iteration algorithm) is used in finite element analysis to solve for the displacement of each mesh element under the load. The change in displacement is used to obtain the displacement values ​​in the x-axis and y-axis directions of each grid cell, forming a deformation displacement field of the interactive semantic region. The deformation displacement field refers to a dataset containing the displacement values ​​in the x-axis and y-axis directions of each grid cell in the interactive semantic region under the action of force. Finally, a background deformation field corresponding to the interactive semantic region is generated based on the deformation displacement field. That is, the displacement values ​​of the grid cells in the deformation displacement field are mapped to the pixel coordinate system of the static background image (the displacement values ​​of the grid cells are assigned to all pixels covered by the grid cells through the correspondence between the grid cells and the pixels of the static background image), and the displacement vector of each pixel (containing the displacement values ​​in the x-axis and y-axis directions) is obtained. Then, the displacement vectors of all pixels together constitute the background deformation field.

[0085] It should be noted that, in this application, the background deformation field refers to a dataset containing the displacement vector of each pixel within the interactive semantic region under the action of force. By determining the background deformation field, the deformation requirements of the interactive semantic region can be transformed into specific displacement instructions for each pixel in the static background image, providing a precise "deformation blueprint" for subsequent background dynamization. This avoids the problem of local distortion or overall incoordination in background deformation due to the lack of a unified displacement standard. In addition, the background deformation field can be generated frame by frame according to the timeline of the character animation sequence. The background deformation field corresponding to each frame can be precisely synchronized with the character's action in that frame, providing a frame-by-frame deformation basis for the subsequent generation of a confident background sequence. This ensures the consistency of the dynamic background and the character's actions in terms of time and space, ultimately making the interaction effect between the character and the background more in line with physical laws and enhancing the realism and visual persuasiveness of the animation.

[0086] In step 104, the background area exposed due to deformation is textured according to the texture features of the interactive semantic region to obtain the synthesized texture of the background area. Then, the background deformation field and the synthesized texture are applied to the static background image to generate a confidence background sequence that evolves synchronously with the character's movements.

[0087] In some embodiments, the process of synthesizing the texture of the background region exposed due to deformation based on the texture features of the interactive semantic region to obtain the synthesized texture of the background region is specifically implemented through the following steps:

[0088] The pixel coordinates of the background area exposed due to deformation are determined based on the dynamic background deformation field.

[0089] Extract texture feature parameters around the interactive semantic region, and then construct a texture feature library;

[0090] Extract effective texture samples of the background region based on the pixel coordinates of the exposed background region;

[0091] The texture feature library is used to perform feature matching on valid texture samples to generate a synthetic texture for the background region.

[0092] In specific implementation, firstly, based on the displacement vector of each pixel in the dynamic background deformation field, the new coordinates of each pixel after deformation are calculated. Then, pixels in the original background image that are not covered and do not belong to the interactive semantic region after deformation are selected as the pixel coordinates of the background region exposed due to deformation. The pixel coordinates of the background region exposed due to deformation refer to the pixel coordinates of the pixels in the static background image after deformation that need to be supplemented with texture. Secondly, the texture sampling range around the interactive semantic region is determined (usually set as the background region extending 10 pixels outward from the edge of the interactive semantic region). Then, a texture feature extraction algorithm (such as the local binary mode algorithm) is used to extract features from the texture within the texture sampling range. All extracted texture feature parameters are organized according to category (such as texture direction, grayscale distribution, frequency features) to form a texture feature library. The texture feature library refers to the library that stores the interactive semantic region. A database of feature parameters of the background texture surrounding the domain is established. Then, with the pixel coordinates of the exposed background region as the center, regions with the same texture style as the exposed background region are extracted from the surrounding undeformed background region as effective texture samples. The effective texture samples refer to image region samples selected from the undeformed background that have the same texture style as the surrounding texture of the exposed background region. Finally, the texture feature parameters of the effective texture samples (extracted by the aforementioned local binary mode algorithm) are compared with the texture feature parameters in the texture feature database to calculate the similarity (e.g., the distance between the two texture feature parameters is calculated using the Euclidean distance algorithm as the similarity). The texture region corresponding to the texture feature parameter with the highest similarity is selected as the reference texture region. The reference texture region is then cropped into a reference template with the same size as the exposed background region, and the reference template is used as the synthetic texture of the background region.

[0093] It should be noted that the synthetic texture in this application refers to the texture template used to fill the exposed background area. By determining the synthetic texture, the background blank exposed by the deformation of the interactive semantic area can be accurately filled, avoiding the blank area from destroying the visual integrity of the animation scene. At the same time, the synthetic texture is generated based on the texture features around the interactive semantic area, ensuring that it is highly consistent with the original background in terms of grayscale distribution, texture structure, and style details, eliminating the problem of texture breakage or misalignment caused by deformation, and ensuring the continuity of the background visuals.

[0094] In some embodiments, applying the background deformation field and the synthesized texture to the static background image to generate a confident background sequence that evolves synchronously with the character's movements is specifically achieved through the following steps:

[0095] Based on the background deformation field, geometric deformation transformation is performed on the interactive semantic region in the static background image to generate a preliminary deformed background frame.

[0096] In the initial deformed background frame, locate the background area exposed by the deformation, and map the synthetic texture to the background area to complete pixel supplementation;

[0097] The edge blending process is performed on the supplemented preliminary deformed background frame to obtain the blended deformed background frame.

[0098] Based on the timeline parameters of the character animation sequence, the fused deformed background frames are arranged in chronological order, and inter-frame interpolation optimization is performed on the chronologically arranged deformed background frames to generate a confidence background sequence that evolves synchronously with the character's movements.

[0099] In specific implementation, firstly, using existing image geometric transformation algorithms (such as affine transformation algorithms, which calculate the new coordinates of pixels after deformation based on the displacement vector of each pixel in the background deformation field, and then determine the grayscale value of each pixel in the deformed image through inverse mapping), displacement transformation is performed on all pixels within the interactive semantic region, while other non-interactive semantic regions in the static background image remain unchanged, ultimately obtaining a preliminary deformed background frame. This preliminary deformed background frame refers to a background image frame where geometric deformation is only performed on the interactive semantic region and the exposed region texture has not yet been supplemented. Secondly, by comparing the pixel coverage of the preliminary deformed background frame with the original static background image, blank areas not covered by the original background pixels in the preliminary deformed background frame (i.e., background areas exposed due to deformation) are selected. Then, each pixel of the synthesized texture is directly filled into the corresponding pixel position of the exposed background area according to the coordinate correspondence, completing the pixel supplementation of the exposed background area, thus obtaining the supplemented background image. The process involves several steps: First, a preliminary deformed background frame is formed, where the supplemented preliminary deformed background frame refers to a background image frame with a synthetic texture filled into the exposed background area. Then, an existing edge smoothing algorithm (such as Gaussian pyramid fusion algorithm) is used to eliminate the color difference between the synthetic texture and the original background texture at the edges, resulting in a fused deformed background frame, which is a single-frame deformed background image with continuous edge texture. Finally, the time axis parameters (such as frame rate and total duration) of the character animation sequence are extracted. Each fused deformed background frame is used as a confidence background frame and mapped to the corresponding time node of the character animation sequence, and then sorted chronologically. An existing inter-frame interpolation algorithm (such as linear interpolation algorithm, which linearly calculates the corresponding pixel displacement values ​​of two adjacent confidence background frames to generate intermediate transition frames) is then used to supplement the missing background frames between frames, thereby obtaining a confidence background sequence that evolves synchronously with the character's movements. The confidence background frame sequence contains multiple background frames.

[0100] It should be noted that the confidence background sequence in this application refers to a valid background image sequence that is synchronized with the timing of the character's actions, formed based on the timeline parameters of the character animation sequence. By determining the dynamic background sequence, dynamic environmental feedback synchronized with the character's actions can be effectively provided for the character animation, transforming the interaction between the character and the background from a separate state of "static background + dynamic character" to a fused state of "background responding in real time with the character's actions". This significantly improves the physical realism and immersion of the animation scene, avoids compositing obstacles caused by mismatch between the timing and frame rate of the background and the character, and ensures the visual refinement of the final target animation.

[0101] In step 105, the target animation is output based on the character animation sequence and the confidence background sequence.

[0102] In some embodiments, the output of the target animation based on the character animation sequence and the confidence background sequence is specifically achieved through the following steps:

[0103] Extract the timestamp information of the character animation sequence and the confidence background sequence;

[0104] Based on the timestamp information, each character frame of the character animation sequence is time-aligned with the background frame corresponding to the confidence background sequence.

[0105] Perform pixel-level fusion on the time-aligned character frames and background frames to generate dynamic composite frames with characters;

[0106] Arrange all dynamic composite frames in chronological order to output the target animation.

[0107] In specific implementation, firstly, the timestamp information (i.e., the start time of each frame) corresponding to each character frame and background frame in the character animation sequence and the confidence background sequence is extracted; secondly, based on the timestamp information, the timestamps of the character animation sequence and the confidence background sequence are traversed, and character frames and background frames with the same timestamp are matched one-to-one, thereby completing the time-series alignment of each character frame in the character animation sequence with the background frame in the confidence background sequence. The time-series aligned character frames and background frames refer to the process of aligning character frames and background frames on the timestamp; then, an image fusion algorithm (such as an Alpha channel fusion algorithm, i.e., if the character frame contains an Alpha channel, the Alpha value of each pixel in the Alpha channel is directly obtained. The Alpha value is directly defined and set by the animation generation software during the material creation stage, and the Alpha value is used to control the transparency of character pixels, i.e. When the alpha value is 1, the character pixels are fully displayed; when it is 0, they are completely transparent. The fused pixel value at each pixel is calculated using the formula "composite frame pixel value = character frame pixel value × alpha value + background frame pixel value × (1 - alpha value)". If the character frame has no alpha channel, the character outline is first extracted using a threshold segmentation algorithm to generate the character's alpha mask, and then fusion is performed according to the above formula. The pixel information of the character frame is combined with the pixel information of the background frame to obtain a dynamic composite frame with the character. The dynamic composite frame with the character refers to a single frame image that blends the character pixels and background pixels to present the character's state in the background. Finally, all dynamic composite frames with the character are arranged sequentially according to the timeline order of the character animation sequence to form a continuous frame sequence. Then, an animation encoding algorithm (such as the H.264 encoding standard) is used to encapsulate the compressed frame sequence into an animation file format (such as MP4) to obtain the target animation.

[0108] It should be noted that, in this application, the target animation film refers to a complete animation file that is composed of dynamic composite frames arranged in chronological order and can be played directly after encoding and encapsulation.

[0109] In another aspect, in some embodiments, this application provides an animation production system, with reference to... Figure 4 The figure is a schematic diagram of the structure of an animation production system according to some embodiments of this application. The animation production system includes: an acquisition module 201, a processing module 202, and an execution module 203, which are described below:

[0110] The acquisition module 201 in this application is mainly used to acquire initial animation production materials containing character animation sequences and static background images;

[0111] Processing module 202, in this application, is mainly used to perform semantic segmentation on the static background image in the initial animation production material, identify the interactive semantic region in the static background image that interacts with the character, and detect the potential contact point between the character and the interactive semantic region based on the character's motion trajectory in the character animation sequence and the geometric parameters of the interactive semantic region.

[0112] The processing module 202 is also used to simulate the deformation effect of the character's actions on the interactive semantic region based on the preset physical attribute parameters of the interactive semantic region when a potential contact point is detected, and generate a corresponding background deformation field.

[0113] In addition, the processing module 202 is also used to perform texture synthesis on the background area exposed due to deformation based on the texture features of the interactive semantic region to obtain the synthesized texture of the background area, and then apply the background deformation field and the synthesized texture to the static background image to generate a confidence background sequence that evolves synchronously with the character's movements.

[0114] The execution module 203 in this application is mainly used to output the target animation film based on the character animation sequence and the confidence background sequence.

[0115] In addition, this application also provides a computer device, the computer device including a memory and a processor, the memory storing code, and the processor being configured to acquire the code and execute the above-described animation production method.

[0116] In some embodiments, reference Figure 5 The figure is a schematic diagram of the structure of a computer device implementing an animation production method according to some embodiments of this application. The animation production method in the above embodiments can be achieved through... Figure 5 The computer device shown is used to implement this, and the computer device includes at least one processor 301, a communication bus 302, a memory 303, and at least one communication interface 304.

[0117] The processor 301 may be a general-purpose central processing unit (CPU), an application-specific integrated circuit (ASIC), or one or more devices used to control the execution of the animation production method in this application.

[0118] The communication bus 302 can be used to transmit information between the aforementioned components.

[0119] The memory 303 may be a read-only memory (ROM) or other type of static storage device capable of storing static information and instructions, random access memory (RAM) or other type of dynamic storage device capable of storing information and instructions, or electrically erasable programmable read-only memory (EEPROM), compact disc read-only memory (CD-ROM) or other optical disc storage, optical disc storage (including compressed optical discs, laser discs, optical discs, digital universal optical discs, Blu-ray discs, etc.), magnetic disks or other magnetic storage devices, or any other medium capable of carrying or storing desired program code in the form of instructions or data structures and accessible by a computer, but not limited thereto. The memory 303 may exist independently and be connected to the processor 301 via the communication bus 302. The memory 303 may also be integrated with the processor 301.

[0120] The memory 303 stores program code for executing the scheme of this application, and its execution is controlled by the processor 301. The processor 301 executes the program code stored in the memory 303. The program code may include one or more software modules. In the above embodiments, the determination of the animation production method can be achieved by the processor 301 and one or more software modules in the program code in the memory 303.

[0121] Communication interface 304 uses any transceiver-like device for communicating with other devices or communication networks, such as Ethernet, radio access network (RAN), wireless local area networks (WLAN), etc.

[0122] In a specific implementation, as one example, a computer device may include multiple processors, each of which may be a single-core (single-CPU) processor or a multi-core (multi-CPU) processor. Here, a processor may refer to one or more devices, circuits, and / or processing cores used to process data (e.g., computer program instructions).

[0123] The aforementioned computer device can be a general-purpose computer device or a special-purpose computer device. In specific implementations, the computer device can be a desktop computer, a portable computer, a network server, a handheld digital assistant (PDA), a mobile phone, a tablet computer, a wireless terminal device, a communication device, or an embedded device. This application does not limit the type of computer device.

[0124] In addition, this application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described animation production method.

[0125] Although preferred embodiments of this application have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments as well as all changes and modifications falling within the scope of this application.

[0126] Obviously, those skilled in the art can make various modifications and variations to this application without departing from the spirit and scope of this application. Therefore, if such modifications and variations fall within the scope of the claims of this application and their equivalents, this application also intends to include such modifications and variations.

Claims

1. An animation production method, characterized in that, Includes the following steps: Obtain initial animation assets containing character animation sequences and static background images; Semantic segmentation is performed on the static background image in the initial animation production material to identify the interactive semantic region in the static background image that interacts with the character. Based on the character's motion trajectory in the character animation sequence and the geometric parameters of the interactive semantic region, potential contact points between the character and the interactive semantic region are detected. When a potential contact point is detected, the deformation effect of the character's actions on the interactive semantic region is simulated based on the preset physical attribute parameters of the interactive semantic region, and a corresponding background deformation field is generated. Based on the texture features of the interactive semantic region, the background region exposed due to deformation is textured to obtain the synthesized texture of the background region. Then, the background deformation field and the synthesized texture are applied to the static background image to generate a confidence background sequence that evolves synchronously with the character's movements. The target animation is output based on the character animation sequence and the confidence background sequence.

2. The method as described in claim 1, characterized in that, Semantic segmentation is performed on the static background images in the initial animation production materials to identify the interactive semantic regions in the static background images that interact with the character. Specifically, these include: The static background image is preprocessed to obtain a preprocessed background image; A pre-trained semantic segmentation model is used to perform pixel-level classification on the preprocessed background image, thereby obtaining segmentation results containing various semantic labels; Candidate semantic regions with deformable characteristics are selected from the segmentation results; Obtain the character animation sequence from the initial animation production materials, and define the interaction detection area based on the movement range of the character animation sequence; Within the interaction detection area, interactive semantic regions with potential interactive relationships with the character are determined from candidate semantic regions with deformable characteristics.

3. The method as described in claim 1, characterized in that, Detecting potential contact points between the character and the interactive semantic region based on the character's motion trajectory in the character animation sequence and the geometric parameters of the interactive semantic region specifically includes: Extract the motion trajectory data of the character in the character animation sequence and the geometric parameters of the interactive semantic region; Based on the motion trajectory data and the geometric parameters, a spatial coordinate system is constructed between the character and the interactive semantic region; Calculate the spatial distance between the character's motion trajectory and the interactive semantic region in the spatial coordinate system; The spatial distance is compared with a preset contact threshold to filter out trajectory points that meet the contact conditions, and these trajectory points that meet the contact conditions are identified as potential contact points between the character and the interactive semantic area.

4. The method as described in claim 1, characterized in that, Based on the texture features of the interactive semantic region, texture synthesis is performed on the background region exposed due to deformation to obtain the synthesized texture of the background region, specifically including: Determine the pixel coordinates of the background area exposed due to deformation based on the background deformation field; Extract texture feature parameters around the interactive semantic region, and then construct a texture feature library; Extract effective texture samples of the background region based on the pixel coordinates of the exposed background region; The texture feature library is used to perform feature matching on valid texture samples to generate a synthetic texture for the background region.

5. The method as described in claim 1, characterized in that, Applying the background deformation field and the synthesized texture to the static background image to generate a confidence background sequence that evolves synchronously with the character's movements specifically includes: Based on the background deformation field, geometric deformation transformation is performed on the interactive semantic region in the static background image to generate a preliminary deformed background frame. In the initial deformed background frame, locate the background area exposed by the deformation, and map the synthetic texture to the background area to complete pixel supplementation; The edge blending process is performed on the supplemented preliminary deformed background frame to obtain the blended deformed background frame. Based on the timeline parameters of the character animation sequence, the fused deformed background frames are arranged in chronological order, and inter-frame interpolation optimization is performed on the chronologically arranged deformed background frames to generate a confidence background sequence that evolves synchronously with the character's movements.

6. The method as described in claim 1, characterized in that, The specific steps for outputting the target animation based on the character animation sequence and the confidence background sequence include: Extract the timestamp information of the character animation sequence and the confidence background sequence; Based on the timestamp information, each character frame of the character animation sequence is time-aligned with the background frame corresponding to the confidence background sequence. Perform pixel-level fusion on the time-aligned character frames and background frames to generate dynamic composite frames with characters; Arrange all dynamic composite frames in chronological order to output the target animation.

7. The method as described in claim 1, characterized in that, The initial animation production materials, including character animation sequences and static background images, are obtained through the animation production data storage.

8. The method as described in claim 1, characterized in that, The character animation sequence consists of multiple character frames.

9. The method as described in claim 1, characterized in that, The initial animation production material is obtained by combining a scale-aligned sequence of character animations and a static background image.

10. An animation production system, characterized in that, The system includes: The acquisition module is used to acquire initial animation materials, including character animation sequences and static background images; The processing module is used to perform semantic segmentation on the static background image in the initial animation production material, identify the interactive semantic region in the static background image that interacts with the character, and detect the potential contact point between the character and the interactive semantic region based on the character's motion trajectory in the character animation sequence and the geometric parameters of the interactive semantic region. The processing module is also used to simulate the deformation effect of the character's actions on the interactive semantic region based on the preset physical attribute parameters of the interactive semantic region when a potential contact point is detected, and generate a corresponding background deformation field. The processing module is further configured to synthesize the texture of the background area exposed due to deformation based on the texture features of the interactive semantic region, to obtain the synthesized texture of the background area, and then apply the background deformation field and the synthesized texture to the static background image to generate a confidence background sequence that evolves synchronously with the character's movements. The execution module is used to output the target animation film based on the character animation sequence and the confidence background sequence.

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