WO2018050001A1 - Method and device for generating animation data - Google Patents

Abstract

A method and device for generating animation data. The method comprises: obtaining a target motion parameter of at least one target animation clip in a target animation to be generated (201); mapping the target motion parameter as a vector matching the input of a pre-trained radial basis function neural network model, and then inputting the vector into the radial basis function neural network model (202); determining components in the vector outputted by the radial basis function neural network model, as fusion weight coefficients of sample animation clips in a sample animation clip sequence (203); according to the fusion weight coefficient, performing fusion by using animation data of the sample animation clips in the sample animation clip sequence, so as to obtain animation data of target animation clips (204); and generating animation data of a target animation according to animation data of the at least one target animation clip (205). Animation data is automatically generated.

Description

Method and device for generating animation data

Cross-reference to related applications

The present application claims the priority of the Chinese Patent Application Serial No. No. 2016.

Technical field

The present application relates to the field of computer technologies, and in particular, to the field of multimedia technologies, and in particular, to a method and apparatus for generating animation data.

Background technique

In the field of animation technology, it is necessary to obtain animation data of various animated images in order to obtain corresponding animations by rendering. In the prior art, an animator is usually required to provide animation data of an animated image to make the formed animated image more realistic.

However, relying on animators to provide animation data requires a lot of manual work. Moreover, the animation data provided by the animator is more limited in use scenes, which is not conducive to many occasions. However, providing animation data for each scene will cause data bloat in the animation process and limit the application of the software. Therefore, it is necessary to reduce the production of animation data and obtain animation data that can automatically adapt to changes in scenes and purposes.

Summary of the invention

The purpose of the present application is to propose a method and apparatus for generating animation data to solve the technical problems mentioned in the background section above.

In a first aspect, the present application provides a method for generating animation data, the method comprising: acquiring a target motion parameter of at least one target video segment in a target animation to be generated; mapping the target motion parameter to The input matching vector of the trained radial basis function neural network model is input to the radial basis function neural network model, wherein the radial basis function neural network model is through various samples in the sequence of sample cartoon segments The cartoon segment is trained; determining each component in the vector output by the radial basis function neural network model as a fusion weight coefficient of each sample animation segment in the sample animation segment sequence; according to the fusion weight coefficient And merging the animation data of each sample animation segment in the sample animation segment sequence to obtain animation data of the target animation segment; and generating animation data of the target animation based on the animation data of the at least one target animation segment.

In some embodiments, each of the sample animation segments in the sequence of sample animation segments is a skeletal animation.

In some embodiments, the method includes the step of training a radial basis function neural network model, the step of training the radial basis function neural network model comprising: sampling a sample of each sample in the sequence of sample animation segments The motion parameters of the cartoon segment are mapped to a first vector, and a second vector is generated according to the order of the sample cartoon segments in the sequence of the sample cartoon segments, wherein the dimension of the second vector is a sample animation segment of the sample animation segment sequence Number, and the component corresponding to the order of the sample animation segments in the second vector is set to 1 and the other components are set to 0; the dimension of the first vector is determined as the number of input layer nodes of the radial basis function neural network model to be trained Determining the number of sample animation segments in the sequence of sample animation segments as the number of nodes in the intermediate hidden layer of the radial basis function neural network model and the number of nodes in the output layer; a vector is used as the input of the radial basis function neural network model, and the second vector corresponding to the sample cartoon segment is taken as Output radial basis function neural network model, the neural network model is trained radial basis function.

In some embodiments, each sample animation segment in the sequence of sample animation segments has been previously divided into at least one time segment according to a key time point; and said each of said sample animation segment sequences is used according to said fusion weight coefficient The animation data of the sample animation segment is fused, comprising: performing weighted averaging on the duration of the time segment in each video animation segment according to the fusion weight coefficient of each sample animation segment to determine the duration of the time segment in the target animation segment; The duration of the time segment of the cartoon segment is adjusted to coincide with the time segment in the target video segment; for each adjusted video animation segment, the fusion parameter is performed on the skeleton parameters of the animation frame in each time segment according to the fusion weight coefficient, The skeleton of the animation frame within the corresponding time segment in the target animation segment parameter.

In some embodiments, the performing a fusion interpolation on a bone parameter of an animation frame in each time segment according to the fusion weight coefficient, including at least one of: performing spherical interpolation on a rotation parameter of each bone in the animation frame; Linear interpolation is performed on the positional parameters of the root bone in the frame.

In some embodiments, before performing the interpolation operation on the skeletal parameters of the animation frames in the respective time segments according to the fused weight coefficients for the adjusted individual sample video segments, the respective Performing fusion interpolation on the bone parameters of the animation frame in the time segment, further comprising: obtaining a horizontal orientation difference and/or a horizontal position difference of the root skeleton of each sample animation segment and the target animation segment; adjusting the horizontal orientation of the root skeleton of each sample animation segment And/or horizontal position to eliminate the horizontal orientation difference and/or horizontal position difference.

In some embodiments, the generating the animation data of the target animation based on the animation data of the at least one target video segment comprises: acquiring a bone parameter of the first animation frame of the current target animation segment and a previous target animation The skeletal parameter of the last animation frame of the segment; according to the skeletal parameters of the two animation frames, the skeletal parameter of the intermediate animation frame to be inserted between the two animation frames is calculated by the interpolation method; between the two animation frames The intermediate animation frame is inserted to generate animation data of the target animation.

In some embodiments, the method further includes: obtaining, for each animation frame in the time segment of the target video segment, a target position parameter of the bone to be corrected in the animation frame; determining a current position parameter and a target position of the bone to be corrected The difference between the parameters; the inverse kinetic iteration is used to adjust the rotation parameters of the bone to be corrected and the associated bone to correct the difference.

In some embodiments, the using the inverse dynamic iteration to iteratively adjust the rotation parameters of the bone to be corrected and the associated bone to correct the difference comprises: acquiring a pre-saved, waiting for the previous animation frame The modified bone uses the inverse dynamic iteration to adjust the adjustment value of the rotation parameter of the bone; the adjustment value is set to the initial adjustment value when the rotation parameter of the bone is adjusted using the inverse dynamic iteration for the current animation frame.

In some embodiments, the method further includes prior to setting the adjustment value to an initial adjustment value when the rotation parameter of the bone is adjusted using the inverse dynamic iteration for the current animation frame. Include: attenuating the adjustment value.

In a second aspect, the present application provides an apparatus for generating animation data, the apparatus comprising: an acquiring unit, configured to acquire target motion parameters of at least one target video segment in a target animation to be generated; and an input unit, configured to: Mapping the target motion parameter to a vector matching the input of the pre-trained radial basis function neural network model, and inputting to the radial basis function neural network model, wherein the radial basis function neural network model is passed a sample obtained by training each sample animation segment in the sample cartoon segment sequence; determining unit configured to determine each component in the vector output by the radial basis function neural network model as each sample animation in the sample animation segment sequence a fusion weighting coefficient of the segment; the fusion unit is configured to use the animation data of each sample animation segment in the sample animation segment sequence to perform animation data according to the fusion weight coefficient, to obtain animation data of the target animation segment; and a generating unit, configured to Animating data of the at least one target cartoon segment to generate the mesh Animation data animation.

In some embodiments, each of the sample animation segments in the sequence of sample animation segments is a skeletal animation.

In some embodiments, the apparatus further includes a training unit for training a radial basis function neural network model, the training unit specifically configured to: for each sample video segment in the sequence of sample animation segments, the sample animation segment The motion parameter is mapped to the first vector, and the second vector is generated according to the order of the sample cartoon segments in the sequence of the sample cartoon segments, wherein the dimension of the second vector is the number of sample animation segments in the sequence of the sample cartoon segments And the component corresponding to the order of the sample cartoon segments in the second vector is set to 1, and the other components are set to 0; the dimension of the first vector is determined as the number of input layer nodes of the radial basis function neural network model to be trained, The number of sample animation segments in the sample animation segment sequence is determined as the number of nodes in the intermediate hidden layer of the radial basis function neural network model and the number of nodes in the output layer; the first vector corresponding to the sample animation segment As an input of the radial basis function neural network model, and using a second vector corresponding to the sample animation segment as the radial basis Output of the neural network model, the radial basis function neural network model is trained.

In some embodiments, each of the sample animation segments in the sequence of sample animation segments has been previously divided into at least one time segment according to a key time point; and the fusion unit, The method includes: a duration determining subunit, configured to perform weighted averaging on durations of time segments in each video animation segment according to a fusion weight coefficient of each sample animation segment to determine a duration of a time segment in the target animation segment; and a duration adjustment subunit, For adjusting the duration of the time segment of each sample animation segment to be consistent with the time segment in the target animation segment; the fusion subunit is configured to use the fusion weight coefficient for each time segment for the adjusted individual sample animation segments The skeletal parameters of the animation frame perform fusion interpolation to obtain the skeletal parameters of the animation frame in the corresponding time segment in the target animation segment.

In some embodiments, the blending subunit is further for at least one of: performing spherical interpolation on rotation parameters of respective bones in the animation frame; performing linear interpolation on positional parameters of the root bone in the animation frame.

In some embodiments, the merging unit further includes a registration subunit, configured to: acquire a horizontal orientation difference and/or a horizontal position difference of a root skeleton of each sample cartoon segment and the target animation segment; and adjust a root of each sample animation segment The horizontal orientation and/or horizontal position of the bone to eliminate the horizontal orientation difference and/or horizontal position difference.

In some embodiments, the generating unit includes: an obtaining subunit, configured to acquire a bone parameter of a first animation frame of the current target video segment and a skeleton parameter of a last animation frame of the previous target video segment; a unit for calculating a bone parameter of an intermediate animation frame to be inserted between the two animation frames according to a bone parameter of two animation frames; inserting a subunit for between the two animation frames The intermediate animation frame is inserted to generate animation data of the target animation.

In some embodiments, the apparatus further includes: a position parameter obtaining unit, configured to acquire, for each animation frame in the time segment of the target video segment, a target position parameter of the bone to be corrected in the animation frame; the difference determining unit, Determining a difference between a current position parameter of the bone to be corrected and a target position parameter; adjusting unit, configured to iteratively adjust a rotation parameter of the bone to be corrected and an associated bone using an inverse dynamics to correct the Describe the difference.

In some embodiments, the adjusting unit includes: an adjustment value acquisition subunit, configured to acquire a pre-saved adjustment value of a rotation parameter of the bone to be corrected using an inverse dynamic iteration for the bone to be corrected of the previous animation frame; Setting a sub-unit for setting the adjustment value to an initial adjustment when the rotation parameter of the bone is adjusted using an inverse dynamic iteration for the current animation frame Integer value.

In some embodiments, the adjusting unit further comprises: an attenuation subunit, configured to: before setting the adjustment value to an initial adjustment value when the rotation parameter of the bone is adjusted using the inverse dynamic iteration for the current animation frame, The adjustment value is attenuated.

The method and device for generating animation data provided by the present application can obtain a target animation segment according to a target motion parameter by using a radial basis function neural network model generated by a sample animation segment sequence and a sample animation segment sequence, and finally form a target animation. The automatic generation of animation data is realized, which reduces the pressure of manual design animation and reduces the data storage pressure.

DRAWINGS

Other features, objects, and advantages of the present application will become more apparent from the detailed description of the accompanying drawings.

1 is an exemplary system architecture diagram to which the present application can be applied;

2 is a flow diagram of one embodiment of a method for generating animation data in accordance with the present application;

3 is a flow chart of still another embodiment of a method for generating animation data in accordance with the present application;

4 is a schematic structural diagram of an embodiment of an apparatus for generating animation data according to the present application;

FIG. 5 is a schematic structural diagram of a computer system suitable for implementing a terminal device or a server of an embodiment of the present application.

detailed description

The present application will be further described in detail below with reference to the accompanying drawings and embodiments. It is understood that the specific embodiments described herein are merely illustrative of the invention, rather than the invention. It is also to be noted that, for the convenience of description, only the parts related to the related invention are shown in the drawings.

It should be noted that the embodiments in the present application and the features in the embodiments may be combined with each other without conflict. The present invention will be described in detail below with reference to the accompanying drawings. Application.

FIG. 1 illustrates an exemplary system architecture 100 of an embodiment of a method and apparatus for generating animation data to which the present application may be applied.

As shown in FIG. 1, system architecture 100 can include terminal devices 101, 102, 103, network 104, and server 105. The network 104 is used to provide a medium for communication links between the terminal devices 101, 102, 103 and the server 105. Network 104 may include various types of connections, such as wired, wireless communication links, fiber optic cables, and the like.

The user can interact with the server 105 over the network 104 using the terminal devices 101, 102, 103 to receive or transmit messages and the like. Various communication client applications, such as animation design software, animation playing software, etc., can be installed on the terminal devices 101, 102, and 103.

The terminal devices 101, 102, 103 may be various electronic devices having a display screen and supporting animation display or animation design, including but not limited to smart phones, tablets, e-book readers, MP3 players (Moving Picture Experts Group Audio Layer) III. The motion picture expert compresses the standard audio layer 3), the MP4 (Moving Picture Experts Group Audio Layer IV) player, the laptop portable computer, the desktop computer, and the like.

The server 105 may be a server that provides various services, such as a background server that provides support for animation displayed on the terminal devices 101, 102, 103. The background server can analyze and process data such as the received animation generation request, and feed back the processing result (for example, animation data) to the terminal device.

It should be noted that the method for generating an animation provided by the embodiment of the present application may be performed by the server 105, or may be performed by the terminal device 101, 102, 103, or may be performed by the server 105 and the terminal devices 101, 102, and 103, respectively. Different steps are performed; accordingly, the means for generating the animation may be provided in the server 105, or may be provided in the terminal device 101, 102, 103, or different units may be provided in the server 105 and the terminal devices 101, 102, 103. .

It should be understood that the number of terminal devices, networks, and servers in Figure 1 is merely illustrative. Depending on the implementation needs, there can be any number of terminal devices, networks, and servers.

With continued reference to FIG. 2, a flow 200 of one embodiment of a method for generating animation data in accordance with the present application is illustrated. The method for generating animation data, including the following step:

Step 201: Acquire a target motion parameter of at least one target video segment in the target animation to be generated.

In this embodiment, the target animation to be generated is an animation that the user wants, and the target animation may be composed of at least one target animation segment. For each target video segment, an electronic device (such as the server or terminal device shown in FIG. 1) on which the method of generating animation data is run can acquire target motion parameters of the target video segment by various methods. When the electronic device is a server, the server may acquire the target motion parameter sent by the terminal device, may also acquire the target motion parameter pre-stored locally in the server, and may obtain the parameter from other servers; when the electronic device is the terminal device, the terminal device The target motion parameter can usually be obtained from the user, or it can be obtained from other devices. The target motion parameter of the target video segment can reflect the motion state of the object in the target video segment when moving. The target motion parameter can be a single parameter or multiple parameters. For example, the target motion parameters in the target animation segment of the walking animation may include forward speed, lateral speed, and cornering speed.

In step 202, the target motion parameter is mapped to a vector matching the input of the pre-trained radial basis function neural network model, and then input to the radial basis function neural network model.

In this embodiment, based on the target motion parameter obtained in step 201, the electronic device may map it to a vector matching the input of the pre-trained radial basis function neural network model, and then input the vector to the radial direction. Basis function in a neural network model. Among them, the radial basis function neural network model is obtained by training each sample animation segment in the sample cartoon segment sequence. In practice, the vector may be bound in advance before the vector formed by the mapping is input to the radial basis function neural network model. That is, when the vector exceeds the range of the training data, the vector is constrained to ensure that the vector input to the radial basis function neural network model does not exceed the range of the training data.

Step 203: Determine each component in the vector output by the radial basis function neural network model as a fusion weight coefficient of each sample animation segment in the sample cartoon segment sequence.

In this embodiment, based on step 202, input to a radial basis function neural network model, the radial basis function neural network model may output a corresponding vector, and the electronic device may determine each component in the output vector to determine a fusion weight coefficient. The weight fusion coefficient is used to confirm The sample animation segments used in the subsequent animation of the sample animation segments in the sequence of sample animation segments and the respective usage ratios are determined.

Step 204: According to the fusion weight coefficient, use the animation data of each sample animation segment in the sample animation segment sequence to perform fusion, and obtain animation data of the target animation segment.

In this embodiment, based on the fusion weight coefficient obtained in step 203, the electronic device can use the animation data of each sample animation segment in the sample cartoon segment sequence to fuse, and the merged animation data can be used as an animation of the corresponding target animation segment. data.

Step 205: Generate animation data of the target animation based on the animation data of the at least one target video segment.

In this embodiment, based on the animation data of the target video segment obtained in step 204, the electronic device may splicing the respective target video segments in order, thereby obtaining animation data of the entire target animation.

In some optional implementations of this embodiment, each sample animation segment in the sequence of sample animation segments is a skeletal animation. Skeletal animation consists of a "bone" that interacts with each other. By controlling the position, direction and size of the bones, and attaching skin data to the bones, you can render the desired visible animated image. In skeletal animation, bones form a hierarchy, the skeleton structure, according to the characteristics of the animated character. The skeletal structure is a series of bones formed by a combination of bones. These bones are organized in a tree structure according to the father-son relationship, forming the entire skeleton frame of the character model. The bone at the root of the tree is called the root bone, which is the key point in the formation of the bone structure. All other bones are called child bones or brother bones, which are attached to the root bone and are connected directly or indirectly to the root bone. In other words, each bone is its parent bone relative to the bones of its next level. In skeletal animation, each bone has two matrices, one is the initial matrix, which is used to represent the initial position of the bone, and the other is the transformation matrix, which reflects the transformation of the bone; multiplied by the initial matrix and the variation matrix. The calculation can get the final matrix of the bone, mainly used to transform the bone. In general, the initial matrix, the transformation matrix, and the final matrix can be characterized either by a relative matrix or by an absolute matrix. The absolute matrix is the matrix of the current bone relative to the world, and the relative matrix is the matrix of the current bone relative to its parent bone. The absolute matrix of the current bone can be obtained by multiplying its relative matrix with the absolute matrix of the parent bone, and the absolute matrix of its parent bone can pass The relative matrix of the parent bone is multiplied by the absolute matrix of the previous bone. Therefore, the iterative multiplication in the above manner up to the root skeleton, the absolute matrix of the current bone can be calculated. Adjacent bones are joined together by joints and can be used for relative movement. When a rotation occurs between the bones, the bones that make up the animated character can make different actions to achieve different animation effects. Compared to vertex animation, skeletal animation only needs to store bone transformation data, and does not need to store data of each vertex in every frame, so using skeletal animation can save a lot of storage space.

In some optional implementations of this embodiment, the method further includes the step of training the radial basis function neural network model. The step of training the radial basis function neural network model specifically includes the following process:

First, for each sample animation segment in the sequence of sample animation segments, the motion parameters of the sample animation segment are mapped to a first vector, and a second vector is generated according to the order of the sample animation segments in the sequence of sample animation segments. The sample animation segment sequence includes at least one sample animation segment, and each sample animation segment may have a corresponding serial number. For example, the sample animation segment sequence includes a sample animation segment 1, a sample animation segment 2, and a sample animation segment n. When the motion parameter of the sample cartoon segment is mapped to the first vector, the animation parameter may include at least one physical quantity, and the value of each physical quantity of the current motion is used as a component to form the first vector. Taking the walking animation as an example, the motion parameters of the walking animation may include three physical quantities such as a forward speed, a lateral speed, and a turning speed, and the first vector to which the sample animation segment is mapped includes the forward speed, the lateral speed, and the turning speed, respectively. The three components corresponding to the value, that is, the dimension of the first vector is 3. It should be noted that the motion parameters in the sample animation segment usually need to be consistent with the target motion parameters in the target animation.

Generating a second vector according to the order of the sample cartoon segments in the sequence of the cartoon segments, the dimension of the second vector is the number of cartoon segments in the sequence of sample cartoon segments, and the component corresponding to the order of the sample animation segments in the second vector is placed 1, other components are set to 0. For example, when the sample animation segment sequence includes the sample animation segment 1, the sample animation segment 2, the sample animation segment n, and the number of sample animation segments is n, the dimension of the second vector corresponding to each sample animation segment is n, ie, The two vectors can be expressed in the form of (X ₁ , X ₂ ... X _n ). For the sample animation segment 1, since its serial number is 1, it is set to 1 on the X ₁ component, and X ₂ ... X _n is set to 0, that is, the second vector corresponding to the sample animation segment 1 is (1, 0...0). ). Correspondingly, the second vector corresponding to the sample animation segment 2 is (0, 1, 0... 0), and the second vector corresponding to the sample animation segment n is (0, 0...0, 1).

Secondly, the dimension of the first vector is determined as the number of input layer nodes of the radial basis function neural network model to be trained, and the number of animation segments in the sequence of sample animation segments is determined as the middle hidden of the radial basis function neural network model. The number of nodes with layers and the number of nodes in the output layer. In the follow-up training, the first vector corresponding to the sample animation segment is used as the input of the radial basis function neural network model and the second vector corresponding to the sample animation segment is used as the output of the radial basis function neural network model, so When the radial basis function neural network model scales, the dimension of the first vector can be determined as the number of input layer nodes of the radial basis function neural network model, and the number of cartoon segments in the sample cartoon segment sequence is determined as the diameter. The number of nodes in the intermediate hidden layer of the basis function neural network model and the number of nodes in the output layer are such that the scale of the radial basis function neural network model matches the size of the first vector and the second vector. Optionally, the intermediate hidden layer of the radial basis function neural network model may adopt a Gaussian kernel function.

Finally, the first vector corresponding to the sample animation segment is used as the input of the radial basis function neural network model, and the second vector corresponding to the sample animation segment is used as the output of the radial basis function neural network model, and the radial basis function neural network The model is trained. Since the scale of the radial basis function neural network model matches the size of the first vector and the second vector, the radial basis function neural network model can be successfully trained by using the first vector and the second vector corresponding to the sample animation segment. The secondary training is to input the first vector and the second vector corresponding to the same sample animation segment as the input and output of the radial basis function neural network model. In the training process, the gradient weighting method is used to train the connection weight between the intermediate hidden layer and the output layer and the width of the hidden layer. That is, during the training process, since the input and output are determined, the function of the intermediate hidden layer is continuously adjusted by the determined input and output.

In some optional implementation manners of the embodiment, each sample animation segment in the sequence of sample animation segments has been divided into at least one time segment according to a key time point in advance; and step 204 specifically includes: first, according to each sample animation segment The weighting coefficient is used to perform weighted averaging on the duration of the time segments in each sample animation segment to determine the duration of the time segment in the target animation segment; secondly, the duration of the time segment of each sample animation segment is adjusted to be in the target animation segment Time segments are consistent; finally, for adjustments Each of the sample animation segments performs fusion interpolation on the skeleton parameters of the animation frames in each time segment according to the fusion weight coefficient, and obtains the skeleton parameters of the animation frames in the corresponding time segments in the target animation segment.

In this implementation, the critical time points can be specifically determined based on the actions represented by the sample animation segments. For example, taking the walking animation as an example, since the walking motion has a significant change in the posture before and after each changing of the foot, and the posture between the two changing feet is gradually changed, the key time point of the walking animation may be each change. The corresponding animation frame at the foot. It is assumed that the walking motion corresponding to the walking animation includes the first step of stepping on the left foot, the second step of stepping on the right foot, the third step of stepping on the left foot, and the fourth step of stepping on the right foot. The actions corresponding to the at least one time segment into which the sample animation segment is divided are the first step, the second step, the third step, and the fourth step, which are respectively divided into the time points of the change of the foot.

Correspondingly, when the fusion is performed in step 204, the skeleton parameters of the animation frames in the corresponding time segments in the target animation segment are respectively calculated using the skeleton parameters of the time segments corresponding to the respective animation segments. First, since the durations of the time segments in the video segments of the samples are not necessarily the same, it is first necessary to determine the duration of the time segments in the target video segment in a certain manner. In this implementation manner, the duration of the time segment in each video clip is weighted and averaged by using the above-mentioned fusion weight coefficient to determine the duration of the time segment in the target video segment. After that, since the fusion parameters need to be performed on the bone parameters of the corresponding animation frames in each time segment in the subsequent fusion process, and the durations of the time segments in each sample animation segment are not necessarily the same, it is necessary to first time the animation segments of each sample. The duration of the clip is adjusted to match the time slice in the target movie clip. Finally, since the adjusted duration of the time segment of each sample animation segment is consistent with the time segment in the target animation segment, the skeletal parameters of the corresponding animation frame of the time segment of the adjusted sample animation segment can be used for the fusion operation to obtain the target The bone parameter of the animation frame within the corresponding time segment in the animation segment.

In some optional implementation manners of the previous implementation, performing the fusion interpolation on the bone parameters of the animation frame in each time segment according to the fusion weight coefficient may include: performing spherical interpolation on the rotation parameters of each bone in the animation frame; Linear interpolation is performed on the positional parameters of the root bone in the frame. For skeletal animation, the bone parameters of each animation frame can include the positional parameters of the root bone and the rotation parameters of each bone. Where the rotation parameters of the bone In the middle, the rotation parameter of the root bone can be represented by the absolute matrix of the root bone. The motion of the non-root bone is mostly the rotation motion relative to its parent bone, and its rotation parameter can be represented by the relative matrix of the bone. Generally, in the animation frame in the skeletal animation, the positional parameter can be represented by a three-dimensional vector, and the rotation parameter can be represented by a four-dimensional vector. Therefore, when the positional parameter is fused and interpolated, linear interpolation can be used for the positional parameter, and for the rotation parameter, the rotation parameter can be used. Spherical interpolation method.

In some optional implementation manners of the embodiment, before performing the interpolation operation on the skeletal parameters of the animation frame in each time segment according to the fused weight coefficient for each of the adjusted video animation segments, the step 204 further includes: first, acquiring The horizontal orientation of the root skeleton of each sample animation segment and the target animation segment is poor and/or horizontally positional; thereafter, the horizontal orientation and/or horizontal position of the root skeleton of each sample animation segment is adjusted to eliminate horizontal orientation differences and/or levels Poor position. In each sample animation segment, the character may be offset between the horizontal and/or horizontal position of the root skeleton and between the target animation segment, and the horizontal orientation and/or horizontal position difference of the root skeleton in the animation segment may be adjusted. So that it is consistent with the horizontal orientation and/or horizontal position in the target animation segment. In general, the horizontal orientation difference and/or the horizontal position difference may be calculated based on the horizontal orientation and/or the horizontal position of the root skeleton in the starting frame of each sample animation segment, and the root skeleton in each animation frame in the sample animation segment. The overall adjustment can be made based on the calculated adjustment parameters.

In some optional implementations of this embodiment, the step 205 specifically includes: acquiring a bone parameter of a first animation frame of the current target animation segment and a skeleton parameter of a last animation frame of the previous target animation segment; The skeletal parameters of the frame, the skeletal parameters of the intermediate animation frame to be inserted between the two animation frames are calculated by the interpolation method; the intermediate animation frame is inserted between the two animation frames to generate the animation data of the target animation.

Since the animation data of each target animation segment has a certain independence from each other, for the two adjacent target animation segments in the target animation, the first animation frame of the previous target animation segment and the first animation of the latter target animation segment The change of the bone parameters between the frames is likely to lack smoothness, so that the action of the target animation will have an action jump at the corresponding time point, reducing the fidelity of the presented action. Based on this, the implementation method calculates the bone parameter of the intermediate animation frame to be inserted between the two animation frames by the interpolation method, and inserts the intermediate animation frame between the two animation frames, thereby enhancing the smoothing of the change between the animation frames. Sex to weaken the jump of the presented action.

The method provided by the above embodiment of the present application can use the radial basis function neural network model generated by the sample cartoon segment sequence and the sample cartoon segment sequence to obtain the target animation segment according to the target motion parameter, and finally form the target animation, and realize the animation data. Automatic generation reduces the pressure on manual design animations and reduces data storage pressure.

With further reference to FIG. 3, a flow 300 of yet another embodiment of a method for generating animation data is shown. The process 300 of the method for generating animation data includes the following steps:

Step 301: Acquire a target motion parameter of at least one target video segment in the target animation to be generated.

In this embodiment, the specific processing of step 301 may refer to step 201 of the corresponding embodiment of FIG. 2.

In step 302, the target motion parameter is mapped to a vector matching the input of the pre-trained radial basis function neural network model, and then input to the radial basis function neural network model.

In this embodiment, the specific processing of step 302 may refer to step 202 of the corresponding embodiment of FIG. 2.

Step 303, determining each component in the vector output by the radial basis function neural network model as a fusion weight coefficient of each sample animation segment in the sample cartoon segment sequence.

In this embodiment, the specific processing of step 303 may refer to step 203 of the corresponding embodiment of FIG. 2.

Step 304: According to the fusion weight coefficient, the animation data of each sample animation segment in the sample animation segment sequence is used to obtain the animation data of the target animation segment.

In this embodiment, in the embodiment, the specific processing of step 304 may refer to step 204 of the corresponding embodiment of FIG. 2 .

Step 305: Generate animation data of the target animation based on the animation data of the at least one target video segment.

In this embodiment, the specific processing of step 305 may refer to step 205 of the corresponding embodiment of FIG. 2.

Step 306, acquiring, for each animation frame in the time segment of the target video segment. The target position parameter of the bone to be corrected in the animation frame.

Taking the walking animation as an example, in the time segment between the character's two foot changes, one of the character's feet should be fixed at the ground position where the foot is initially pressed. Since the target video segment is formed by the merging operation, the foot in the presented action may be displaced within the time segment, and the sliding step phenomenon occurs, thereby affecting the realisticness of the action presented by the target animation. Therefore, it is necessary to amend it.

In this embodiment, after determining the bone that needs to be corrected in each animation frame of the animation segment according to the user operation, the electronic device may acquire the target position parameter to which the bone to be corrected needs to be corrected. The target location parameter can be set by the user or automatically according to pre-configured rules.

Step 307, determining a difference between a current position parameter of the bone to be corrected and a target position parameter.

In this embodiment, for the bone to be repaired, the electronic device may calculate the current position parameter and the target based on the target position parameter acquired in step 306 and the current position parameter of the bone to be repaired in the target animation segment generated by the foregoing steps. The difference between the positional parameters to be used as a parameter for the subsequent correction process.

In step 308, the inverse kinetic iteration is used to adjust the rotation parameters of the bone to be corrected and the associated bone to correct the difference.

In this embodiment, based on the difference obtained in step 307, the electronic device can iteratively adjust the rotation parameters of the bone to be corrected and the associated bone by inverse dynamics, and correct the difference so that the bone to be corrected is in this The process gradually approaches the target position. The associated bones may be set by the user, or may be determined according to the bone to be corrected and certain rules. For example, when adjusting the above-mentioned sliding phenomenon, when adjusting the position of the foot, it is necessary to keep the torso (root skeleton) at the same time, so the bones that need to adjust the rotation parameters include the bone corresponding to the foot (the bone to be corrected), the calf and The two bones (associated bones) corresponding to the thighs. Since the final target of the rotation parameter of the adjustment bone is the position of the bone to be corrected, it is possible to perform an iterative adjustment to the parent bone of the current bone from the bone to be corrected which is most closely related to the correction target by the inverse dynamic method. The correction of the above difference is completed. The specific algorithm of inverse dynamics is not described here.

It should be noted that, step 306 to step 308, generally after step 304 Execution may also be performed after step 305.

In some optional implementation manners of the embodiment, the step 308 may include: acquiring, in advance, an adjustment value of the rotation parameter of the bone to be corrected using the inverse dynamic iteration for the bone to be corrected of the previous animation frame; Set to the initial adjustment value when using the inverse dynamic iteration to adjust the rotation parameters of the bone for the current animation frame.

In this implementation, the adjustment value of the bone rotation parameter in the previous animation frame can be adjusted by the inverse dynamics as an initial adjustment value when the rotation parameter of the bone is adjusted using the inverse dynamic iteration for the current animation frame, The skeletal parameter difference between adjacent animation frames in the time segment that needs to be corrected in the animation segment obtained by the foregoing steps is small, and the adjustment value of the bone rotation parameter in the inverse dynamic kinetic animation frame starts from a small The number of iterations can be used to correct the positional parameters of the bone to be corrected in the current animation frame, which improves the calculation efficiency and reduces the time spent.

In some optional implementations of the previous implementation, before the adjustment value is set to an initial adjustment value when the rotation parameter of the bone is adjusted using the inverse dynamic iteration for the current animation frame, the method further includes: performing the adjustment value attenuation. Generally, there are some differences in the bone rotation parameters between adjacent animation frames in the time segment. Therefore, the adjustment value of the previous animation frame can be attenuated according to the law, thereby further improving the stability of the animation.

As can be seen from FIG. 3, the flow 300 of the method for generating animation data in the present embodiment increases the step of correcting the position of the bone in the animation frame as compared with the embodiment corresponding to FIG. 2, further improving The fidelity of the generated animation data.

With further reference to FIG. 4, as an implementation of the method shown in the above figures, the present application provides an embodiment of an apparatus for generating animation data, the apparatus embodiment corresponding to the method embodiment shown in FIG. The device can be specifically applied to various electronic devices.

As shown in FIG. 4, the apparatus 400 for generating animation data according to the embodiment includes an acquisition unit 401, an input unit 402, a determination unit 403, a fusion unit 404, and a generation unit 405. The acquiring unit 401 is configured to acquire target motion parameters of at least one target video segment in the target animation to be generated, and the input unit 402 is configured to map the target motion parameter to match the input of the pre-trained radial basis function neural network model. Vector after losing Into the radial basis function neural network model, wherein the radial basis function neural network model is obtained by training each sample animation segment in the sample cartoon segment sequence; the determining unit 403 is configured to output the radial basis function neural network model Each component in the vector is determined as a fusion weight coefficient of each sample animation segment in the sequence of sample animation segments; the fusion unit 404 is configured to use the animation data of each sample animation segment in the sample animation segment sequence to fuse according to the fusion weight coefficient, and obtain the target The animation data of the cartoon segment; and the generating unit 405 is configured to generate animation data of the target animation based on the animation data of the at least one target video segment.

In this embodiment, the specific processing of the obtaining unit 401, the input unit 402, the determining unit 403, the merging unit 404, and the generating unit 405 may refer to step 201, step 202, step 203, step 204 in the corresponding embodiment of FIG. 2, respectively. Step 205, which will not be described again here.

In some optional implementations of this embodiment, each sample animation segment in the sequence of sample animation segments is a skeletal animation.

In some optional implementations of this embodiment, the apparatus 400 further includes a training unit (not shown) for training a radial basis function neural network model, the training unit being specifically configured to: for each sample in the sequence of sample animation segments The animation segment maps the motion parameters of the sample animation segment to the first vector, and generates a second vector according to the order of the sample cartoon segments in the sequence of the sample cartoon segments, wherein the dimension of the second vector is the sample animation segment in the sequence of the sample cartoon segments The number of components in the second vector corresponding to the order of the sample animation segments is set to 1 and the other components are set to 0; the dimensions of the first vector are determined as the input layer nodes of the radial basis function neural network model to be trained. The number of the sample animation segments in the sample animation segment sequence is determined as the number of nodes in the intermediate hidden layer of the radial basis function neural network model and the number of nodes in the output layer; the first vector corresponding to the sample animation segment is taken as The input of the radial basis function neural network model, and the second vector corresponding to the video animation segment as the radial basis function neural network The output of the model trains the radial basis function neural network model.

In some optional implementation manners of the embodiment, each sample animation segment in the sequence of sample animation segments has been divided into at least one time segment according to a key time point in advance; and the fusion unit 404 includes: a duration determination subunit (not shown) ), for the duration of the time segment in each sample animation segment according to the fusion weight coefficient of each sample animation segment Performing a weighted average to determine the duration of the time segment in the target video segment; a duration adjustment sub-unit (not shown) for adjusting the duration of the time segment of each sample animation segment to coincide with the time segment in the target animation segment; a sub-unit (not shown) is configured to perform fused interpolation on the skeletal parameters of the animation frame in each time segment according to the fused weight coefficient for the adjusted individual sample video segments, to obtain an animation frame in the corresponding time segment in the target video segment. Skeleton parameters.

In some optional implementations of this embodiment, the fusion subunit is further used for at least one of: performing spherical interpolation on rotation parameters of respective bones in the animation frame; and performing linear interpolation on positional parameters of the root bone in the animation frame.

In some optional implementations of this embodiment, the fusion unit 404 further includes a registration subunit (not shown) for: obtaining a horizontal orientation difference and/or level of the root skeleton of each sample animation segment and the target animation segment. Position difference; adjust the horizontal orientation and/or horizontal position of the root bone of each sample animation segment to eliminate horizontal orientation differences and/or horizontal position differences.

In some optional implementations of this embodiment, the generating unit 405 includes: an obtaining subunit (not shown) for acquiring a bone parameter of the first animation frame of the current target video segment and a last of the previous target video segment. a skeleton parameter of an animation frame; a calculation subunit (not shown) for calculating a bone parameter of an intermediate animation frame to be inserted between two animation frames by an interpolation method according to a bone parameter of the two animation frames; inserting the subunit (not shown) for inserting an intermediate animation frame between two animation frames to generate animation data of the target animation.

In some optional implementations of this embodiment, the apparatus 400 further includes: a position parameter acquisition unit (not shown), configured to acquire, for each animation frame in the time segment of the target video segment, the skeleton to be corrected in the animation frame. a target position parameter; a difference determining unit (not shown) for determining a difference between a current position parameter of the bone to be corrected and a target position parameter; an adjusting unit (not shown) for iterating using the inverse dynamics Adjust the rotation parameters of the bone to be corrected and the associated bone to correct the difference.

In some optional implementation manners of the embodiment, the adjusting unit includes: an adjustment value acquisition subunit (not shown), configured to acquire a pre-stored inverse dynamics iterative adjustment for the bone to be corrected of the previous animation frame. The adjustment value of the rotation parameter of the bone; a set subunit (not shown) for setting the adjustment value to the initial adjustment value when the rotation parameter of the bone is adjusted using the inverse dynamic iteration for the current animation frame.

In some optional implementations of this embodiment, the adjusting unit further includes: an attenuation subunit (not shown) for setting the adjustment value to use the inverse dynamic iteration to adjust the rotation parameter of the bone to the current animation frame The adjustment value is attenuated before the initial adjustment value.

Referring now to Figure 5, there is shown a block diagram of a computer system 500 suitable for use in implementing a terminal device or server of an embodiment of the present application.

As shown in FIG. 5, computer system 500 includes a central processing unit (CPU) 501 that can be loaded into a program in random access memory (RAM) 503 according to a program stored in read only memory (ROM) 502 or from storage portion 508. And perform various appropriate actions and processes. In the RAM 503, various programs and data required for the operation of the system 500 are also stored. The CPU 501, the ROM 502, and the RAM 503 are connected to each other through a bus 504. An input/output (I/O) interface 505 is also coupled to bus 504.

The following components are connected to the I/O interface 505: an input portion 506 including a keyboard, a mouse, etc.; an output portion 507 including, for example, a cathode ray tube (CRT), a liquid crystal display (LCD), and the like, and a storage portion 508 including a hard disk or the like. And a communication portion 509 including a network interface card such as a LAN card, a modem, or the like. The communication section 509 performs communication processing via a network such as the Internet. Driver 510 is also coupled to I/O interface 505 as needed. A removable medium 511 such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory or the like is mounted on the drive 510 as needed so that a computer program read therefrom is installed into the storage portion 508 as needed.

In particular, the processes described above with reference to the flowcharts may be implemented as a computer software program in accordance with an embodiment of the present disclosure. For example, an embodiment of the present disclosure includes a computer program product comprising a computer program tangibly embodied on a machine readable medium, the computer program comprising program code for executing the method illustrated in the flowchart. In such an embodiment, the computer program can be downloaded and installed from the network via the communication portion 509, and/or installed from the removable medium 511.

The flowchart and block diagrams in the Figures illustrate the architecture, functionality and operation of possible implementations of systems, methods and computer program products in accordance with various embodiments of the present application. In this regard, each block of the flowchart or block diagram can represent a module, a program segment, or a portion of code, the module, the program segment, or a portion of code comprising one or more An executable instruction that implements the specified logic functions. It should also be noted that in some alternative implementations, the functions noted in the blocks may also occur in a different order than that illustrated in the drawings. For example, two successively represented blocks may in fact be executed substantially in parallel, and they may sometimes be executed in the reverse order, depending upon the functionality involved. It is also noted that each block of the block diagrams and/or flowcharts, and combinations of blocks in the block diagrams and/or flowcharts, can be implemented in a dedicated hardware-based system that performs the specified function or operation. Or it can be implemented by a combination of dedicated hardware and computer instructions.

The units involved in the embodiments of the present application may be implemented by software or by hardware. The described unit may also be provided in the processor, for example, as a processor including an acquisition unit, an input unit, a determination unit, a fusion unit, and a generation unit. Wherein, the names of the units do not constitute a limitation on the unit itself in some cases. For example, the obtaining unit may also be described as “a unit that acquires target motion parameters of at least one target video segment in the target animation to be generated”. .

In another aspect, the present application further provides a non-volatile computer storage medium, which may be a non-volatile computer storage medium included in the apparatus described in the foregoing embodiments; It may be a non-volatile computer storage medium that exists alone and is not assembled into the terminal. The non-volatile computer storage medium stores one or more programs, when the one or more programs are executed by a device, causing the device to: acquire target motion of at least one target video segment in the target animation to be generated a parameter; mapping the target motion parameter to a vector matching the input of the pre-trained radial basis function neural network model, and inputting to the radial basis function neural network model, wherein the radial basis function neural network model It is obtained by training each sample animation segment in the sequence of sample animation segments; determining each component in the vector output by the radial basis function neural network model as the fusion of each sample animation segment in the sequence of the sample animation segment Weighting coefficient; according to the fusion weight coefficient, using the animation data of each sample animation segment in the sample animation segment sequence to obtain animation data of the target animation segment; based on the animation data of the at least one target animation segment, generating the The animation data of the target animation.

The above description is only the preferred embodiment of the present application and the principle of the applied technology. Bright. It should be understood by those skilled in the art that the scope of the invention referred to in the present application is not limited to the specific combination of the above technical features, and should also be covered by the above technical features without departing from the inventive concept. Other technical solutions formed by any combination of their equivalent features. For example, the above features are combined with the technical features disclosed in the present application, but are not limited to the technical features having similar functions.

Claims (22)

1. A method for generating animation data, the method comprising:

   Obtaining a target motion parameter of at least one target video segment in the target animation to be generated;

   Mapping the target motion parameter to a vector matching the input of the pre-trained radial basis function neural network model, and inputting to the radial basis function neural network model, wherein the radial basis function neural network model is passed Training each sample animation segment in the sample cartoon segment sequence;

   Determining, in the vector output by the radial basis function neural network model, a fusion weight coefficient of each sample animation segment in the sequence of sample animation segments;

   According to the fusion weight coefficient, using the animation data of each sample animation segment in the sample animation segment sequence to perform fusion, to obtain animation data of the target animation segment;

   Generating animation data of the target animation based on animation data of the at least one target video segment.
2. The method of claim 1 wherein each of the sample animation segments in the sequence of sample animation segments is a skeletal animation.
3. The method of claim 1 or 2, wherein the method comprises the step of training a radial basis function neural network model, the step of training the radial basis function neural network model comprising:

   Mapping a motion parameter of the sample animation segment to a first vector for each sample animation segment in the sample animation segment sequence, and generating a second vector according to an order of the sample animation segment in the sample animation segment sequence, wherein the second The dimension of the vector is the number of sample animation segments in the sequence of sample animation segments, and the component corresponding to the order of the sample animation segments in the second vector is set to 1, and the other components are set to 0;

   Determining the dimension of the first vector as the number of input layer nodes of the radial basis function neural network model to be trained, and determining the number of sample animation segments in the sequence of the sample animation segments as the radial basis function neural network model Number of nodes in the middle hidden layer and output layer Number of nodes;

   Taking the first vector corresponding to the sample cartoon segment as the input of the radial basis function neural network model, and using the second vector corresponding to the sample animation segment as the output of the radial basis function neural network model, for the radial direction The basis function neural network model is trained.
4. The method according to claim 2, wherein each of the sample animation segments in the sequence of sample animation segments has been previously divided into at least one time segment according to a key time point;

   Performing, according to the fusion weight coefficient, the animation data of each sample animation segment in the sample animation segment sequence, including:

   Performing a weighted average of the durations of the time segments in each of the video segments according to the blending weight coefficients of the video segments of each sample to determine the duration of the time segments in the target video segment;

   Adjusting the duration of the time segment of each sample animation segment to coincide with the time segment in the target animation segment;

   For the adjusted individual video clips, the fused parameter is performed on the skeletal parameters of the animation frames in each time segment according to the fusion weight coefficient, and the skeletal parameters of the animation frames in the corresponding time segments in the target animation segment are obtained.
5. The method according to claim 4, wherein said performing fusion interpolation on a bone parameter of an animation frame in each time segment according to said fusion weight coefficient comprises at least one of the following:

   Performing spherical interpolation on the rotation parameters of each bone in the animation frame;

   Perform linear interpolation on the positional parameters of the root bone in the animation frame.
6. The method according to claim 4, wherein said step of performing an interpolation operation on the skeleton parameters of the animation frames in each time segment according to said fusion weight coefficient for said adjusted individual sample video segments The fusion weight coefficient performs fusion interpolation on the bone parameters of the animation frame in each time segment, and further includes:

   Obtaining a horizontal orientation difference and/or a horizontal position difference of a root skeleton of each sample animation segment and the target animation segment;

   The horizontal orientation and/or horizontal position of the root bone of each sample animation segment is adjusted to eliminate the horizontal orientation difference and/or horizontal position difference.
7. The method according to claim 2, wherein the generating animation data of the target animation based on the animation data of the at least one target video segment comprises:

   Get the bone parameter of the first animation frame of the current target animation segment and the bone parameter of the last animation frame of the previous target animation segment;

   Calculating a bone parameter of an intermediate animation frame to be inserted between the two animation frames by an interpolation method according to a bone parameter of two animation frames;

   The intermediate animation frame is inserted between the two animation frames to generate animation data of the target animation.
8. The method of claim 2, wherein the method further comprises:

   Obtaining a target position parameter of the bone to be corrected in the animation frame for each animation frame in the time segment of the target cartoon segment;

   Determining a difference between a current position parameter of the bone to be corrected and a target position parameter;

   The inverse kinetic iteration is used to adjust the rotation parameters of the bone to be corrected and the associated bone to correct the difference.
9. The method according to claim 8, wherein the iteratively adjusting the rotation parameters of the bone to be corrected and the associated bone using an inverse dynamic iteration to correct the difference comprises:

   Obtaining an adjustment value of a rotation parameter of the bone that is pre-saved and for the bone to be corrected of the previous animation frame using an inverse dynamic iteration;

   The adjustment value is set to an initial adjustment value when the rotation parameters of the bone are adjusted using the inverse dynamic iteration for the current animation frame.
10. The method of claim 9 wherein the initial adjustment is performed to adjust the rotation parameters of the bone using an inverse dynamic iteration of the current animation frame. Before the initial adjustment value, the method further includes:

    The adjustment value is attenuated.
11. An apparatus for generating animation data, the apparatus comprising:

    An acquiring unit, configured to acquire a target motion parameter of at least one target video segment in the target animation to be generated;

    An input unit, configured to map the target motion parameter to a vector matching a input of a pre-trained radial basis function neural network model, and input the result to the radial basis function neural network model, wherein the radial basis function The neural network model is obtained by training each sample animation segment in the sequence of sample animations;

    a determining unit, configured to determine each component in the vector output by the radial basis function neural network model as a fusion weight coefficient of each sample animation segment in the sequence of sample animation segments;

    a merging unit, configured to perform merging using the animation data of each sample animation segment in the sequence of sample animation segments according to the fusion weight coefficient, to obtain animation data of the target animation segment;

    And a generating unit, configured to generate animation data of the target animation based on the animation data of the at least one target video segment.
12. The apparatus according to claim 11, wherein each of the sample animation segments in the sequence of sample animation segments is a skeletal animation.
13. The apparatus according to claim 11 or 12, further comprising a training unit for training a radial basis function neural network model, the training unit being specifically configured to:

    Mapping a motion parameter of the sample animation segment to a first vector for each sample animation segment in the sample animation segment sequence, and generating a second vector according to an order of the sample animation segment in the sample animation segment sequence, wherein the second The dimension of the vector is the number of sample animation segments in the sequence of sample animation segments, and the component corresponding to the order of the sample animation segments in the second vector is set to 1, and the other components are set to 0;

    Determining the dimension of the first vector as the radial basis function neural network model to be trained Entering the number of nodes, determining the number of sample animation segments in the sequence of sample animation segments as the number of nodes in the intermediate hidden layer of the radial basis function neural network model and the number of nodes in the output layer;

    Taking the first vector corresponding to the sample cartoon segment as the input of the radial basis function neural network model, and using the second vector corresponding to the sample animation segment as the output of the radial basis function neural network model, for the radial direction The basis function neural network model is trained.
14. The apparatus according to claim 12, wherein each of the sample animation segments in the sequence of sample animation segments has been previously divided into at least one time segment according to a key time point;

    The fusion unit includes:

    The duration determining subunit is configured to perform weighted averaging on the duration of the time segments in each of the sample animation segments according to the fusion weight coefficient of each sample animation segment to determine the duration of the time segment in the target animation segment;

    a time adjustment subunit for adjusting the duration of the time segment of each sample animation segment to be consistent with the time segment in the target animation segment;

    a fusion subunit, configured to perform fused interpolation on the skeletal parameters of the animation frames in each time segment according to the fused weight coefficients for the adjusted individual sample animation segments, to obtain the skeleton of the animation frame in the corresponding time segment in the target animation segment parameter.
15. The apparatus according to claim 14, wherein said fusion subunit is further used for at least one of the following:

    Performing spherical interpolation on the rotation parameters of each bone in the animation frame;

    Perform linear interpolation on the positional parameters of the root bone in the animation frame.
16. The apparatus according to claim 14, wherein said merging unit further comprises a registration subunit, said registration subunit being:

    Obtaining a horizontal orientation difference and/or a horizontal position difference of a root skeleton of each sample animation segment and the target animation segment;

    Adjust the horizontal orientation and/or horizontal position of the root bone of each sample animation segment to eliminate In addition to the horizontal orientation difference and/or horizontal position difference.
17. The device according to claim 12, wherein the generating unit comprises:

    Obtaining a subunit, configured to acquire a bone parameter of a first animation frame of the current target animation segment and a skeleton parameter of a last animation frame of the previous target animation segment;

    a calculating subunit, configured to calculate a bone parameter of an intermediate animation frame to be inserted between the two animation frames by an interpolation method according to a bone parameter of the two animation frames;

    Inserting a subunit for inserting the intermediate animation frame between the two animation frames to generate animation data of the target animation.
18. The device according to claim 12, further comprising:

    a position parameter obtaining unit, configured to acquire a target position parameter of the bone to be corrected in the animation frame for each animation frame in the time segment of the target video segment;

    a difference determining unit, configured to determine a difference between a current position parameter of the bone to be corrected and a target position parameter;

    An adjustment unit for iteratively adjusting the rotation parameters of the bone to be corrected and the associated bone using inverse dynamics to correct the difference.
19. The apparatus according to claim 18, wherein the adjustment unit comprises:

    The adjustment value acquisition sub-unit is configured to obtain a pre-saved adjustment value of the rotation parameter of the bone to be corrected using the inverse dynamic iteration for the bone to be corrected of the previous animation frame;

    A sub-unit is provided for setting the adjustment value to an initial adjustment value when the rotation parameter of the bone is adjusted using the inverse dynamic iteration for the current animation frame.
20. The apparatus according to claim 19, wherein the adjusting unit further comprises:

    An attenuation subunit, configured to adjust the value before setting the initial adjustment value when the rotation parameter of the bone is adjusted using the inverse dynamic iteration for the current animation frame Attenuate.
21. A device that includes:

    Processor; and

    Memory,

    The memory stores computer readable instructions executable by the processor, the processor executing the method of any of claims 1-10 when the computer readable instructions are executed.
22. A non-volatile computer storage medium storing computer readable instructions executable by a processor, the processor executing as claimed in claim 1 The method of any of ten.

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