Animation generation method and device, storage medium and electronic device

Abstract

The present disclosure relates to an animation generation method and device, a storage medium and an electronic device, the method comprising: inputting a magnetic resonance imaging (MRI) video to be animated into an animation generation model; based on the MRI video, outputting an animation video of a pronunciation organ corresponding to the pronunciation organ in the MRI video. The present disclosure can convert an MRI video into an animation video, making the position, shape, state, etc. of the pronunciation organ in the MRI video clearer, thereby improving the efficiency of users extracting information from the MRI video.

Description

Technical Field

[0001] The present disclosure relates to the field of animation, and in particular, to an animation generation method and device, a storage medium, and an electronic device. Background Art

[0002] MRI (Magnetic Resonance Imaging) is a technology that uses the magnetic resonance phenomenon to obtain electromagnetic signals from the human body and reconstruct internal images. MRI images can clearly observe the position, shape and state of the human body's internal organs, and are therefore widely used in the medical field.

[0003] However, due to the lack of clarity in MRI images and the lack of familiarity of non-professionals with the shapes of organs, it is difficult for the general public to extract information from MRI images or MRI videos, which limits the application of MRI in other fields. Summary of the invention

[0004] This summary is provided to introduce concepts in a brief form that will be described in detail in the detailed description below. This summary is not intended to identify key features or essential features of the claimed technical solution, nor is it intended to limit the scope of the claimed technical solution.

[0005] In a first aspect, the present disclosure provides an animation generation method, comprising: inputting a magnetic resonance imaging (MRI) video for generating an animation into an animation generation model; and outputting a pronunciation organ animation video corresponding to the pronunciation organ in the MRI video based on the MRI video.

[0006] In a second aspect, the present disclosure provides an animation generation device, comprising: an input module for inputting a magnetic resonance imaging (MRI) video to be animated into an animation generation model; and an acquisition module for acquiring an animation video of a pronunciation organ output by the animation generation model based on the pronunciation organs in the MRI video.

[0007] In a third aspect, the present disclosure provides a computer-readable medium having a computer program stored thereon, which, when executed by a processing device, implements the steps of the method described in the first aspect of the present disclosure.

[0008] In a fourth aspect, the present disclosure provides an electronic device, comprising: a storage device and a processing device, wherein a computer program is stored in the storage device, and the processing device is used to execute the computer program in the storage device to implement the steps of the method described in the first aspect of the present disclosure.

[0009] Through the above technical solution, at least the following technical effects can be achieved:

[0010] By inputting the MRI video into the animation generation model and outputting a pronunciation organ animation video corresponding to the pronunciation organs in the MRI video based on the MRI video, the MRI video can be converted into an animation video, making the position, shape, state, etc. of the pronunciation organs in the MRI video clearer, thereby improving the efficiency of users in extracting information from the MRI video.

[0011] Other features and advantages of the present disclosure will be described in detail in the following detailed description. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] The above and other features, advantages and aspects of the embodiments of the present disclosure will become more apparent with reference to the following detailed description in conjunction with the accompanying drawings. Throughout the drawings, the same or similar reference numerals represent the same or similar elements. It should be understood that the drawings are schematic and the originals and elements are not necessarily drawn to scale. In the drawings:

[0013] Figure 1 The invention is a flowchart of an animation generation method according to an exemplary disclosed embodiment.

[0014] Figure 2 It is a schematic diagram of a training process of an animation generation model according to an exemplary disclosed embodiment.

[0015] Figure 3 It is a block diagram of an animation generating device according to an exemplary disclosed embodiment.

[0016] Figure 4 It is a block diagram of an electronic device according to an exemplary disclosed embodiment. DETAILED DESCRIPTION

[0017] Embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although certain embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be construed as being limited to the embodiments described herein, which are instead provided for a more thorough and complete understanding of the present disclosure. It should be understood that the drawings and embodiments of the present disclosure are only for exemplary purposes and are not intended to limit the scope of protection of the present disclosure.

[0018] It should be understood that the various steps described in the method embodiments of the present disclosure may be performed in different orders and / or in parallel. In addition, the method embodiments may include additional steps and / or omit the steps shown. The scope of the present disclosure is not limited in this respect.

[0019] The term "including" and its variations used herein are open inclusions, i.e., "including but not limited to". The term "based on" means "based at least in part on". The term "one embodiment" means "at least one embodiment"; the term "another embodiment" means "at least one additional embodiment"; the term "some embodiments" means "at least some embodiments". The relevant definitions of other terms will be given in the following description.

[0020] It should be noted that the concepts such as "first" and "second" mentioned in the present disclosure are only used to distinguish different devices, modules or units, and are not used to limit the order or interdependence of the functions performed by these devices, modules or units.

[0021] It should be noted that the modifications of "one" and "plurality" mentioned in the present disclosure are illustrative rather than restrictive, and those skilled in the art should understand that unless otherwise clearly indicated in the context, it should be understood as "one or more".

[0022] The names of the messages or information exchanged between multiple devices in the embodiments of the present disclosure are only used for illustrative purposes and are not used to limit the scope of these messages or information.

[0023] Figure 1 is a flowchart of an animation generation method according to an exemplary disclosed embodiment. Figure 1 As shown, the method comprises the following steps:

[0024] S11, inputting the MRI video of the to-be-animated image into the animation generation model.

[0025] MRI videos are composed of multiple video frames. When generating animations, you can choose to input all the video frames into the animation generation model. After obtaining the animation frames output by the animation generation model, you can reorganize the animation frames according to the arrangement order of the video frames to obtain the animation video corresponding to the video frames.

[0026] In a possible implementation, video frames can be selected at intervals of preset frames and input into the animation generation model. In this way, after obtaining the animation frames generated by the animation generation model, interpolation can be performed between the animation frames to generate a smooth animation video. In this way, the workload of the animation generation model can be reduced, the consumption of computing resources can be reduced, and the efficiency of animation generation can be improved.

[0027] The animation generation model can be any machine learning model that can learn samples, such as a generative adversarial network model, a recurrent neural network model, a convolutional network model, etc., and the present disclosure does not limit this. The training samples of the model include multiple MRI sample images and animated pronunciation organ maps corresponding to each MRI sample image. By learning the training samples, the animation generation model can generate corresponding animation images based on the input MRI images, thereby achieving the effect of converting MRI video frames into animation frames.

[0028] S12, based on the MRI video, outputting an animation video of the pronunciation organs corresponding to the pronunciation organs in the MRI video. The animation generation model can sequentially output animation frames corresponding to the video frames in the order of the video frames input, and combine them into an animation video of the pronunciation organs. The positions of the pronunciation organs in the animation frames are filled with the animation pronunciation organs, which is convenient for users to view and understand.

[0029] In a possible implementation, different colors can be filled for each animated pronunciation organ according to the difference of the pronunciation organs, and the organ name can be marked on the animated pronunciation organ. For example, the upper palate position can be filled with light yellow and marked with the character "palate", the tongue position can be filled with bright red and marked with the character "tongue", and the teeth position can be filled with white and marked with the character "teeth". In this way, the position and connection relationship of each pronunciation organ can be more intuitively reflected, which is more conducive to user understanding.

[0030] It is worth noting that the above-mentioned color filling method and name marking method are only used as an example for explanation. The present disclosure does not limit the organ color filling method and name marking method. For example, the name marking can also be marked in a foreign language, or the phonetic symbols, pinyin, etc. of the pronunciation can be added. The animation frames are reorganized according to the arrangement order of the video frames to obtain a complete animation video. The playback speed of the animation frames can be consistent with the video frames, and the playback speed of the animation frames can also be adjusted according to application requirements. For example, when the animation video is used in an educational scenario, in order to more clearly show the movement and force conditions of the pronunciation organs, the playback speed of the animation frames can be reduced. In the case of reducing the playback speed of the animation frames, in order to make the animation video smoother, frames can also be added between frames to increase the number of frames of the animation video.

[0031] In a possible implementation, the animation generation model is a generative adversarial network model, the animation generation model includes a generator for generating animation images based on MRI images, and the animation generation model is trained in the following manner:

[0032] Repeat the steps of generating a training animation image based on the MRI sample image by the generator, generating a loss value based on the animation pronunciation organ map corresponding to the MRI sample image and a preset loss function, adjusting the parameters in the generator based on the loss value, and evaluating the training animation image based on the animator pronunciation organ map by the discriminator of the generative adversarial network model, until the evaluation result meets the preset evaluation result condition.

[0033] The generator is used to generate images based on input data, and the discriminator is used to evaluate whether the image output by the generator has consistent features with the images in the specified set, that is, it can determine whether the image is in the specified set. The evaluation result of the discriminator may be correct or wrong. When the features of the image output by the generator are obviously different from those of the images in the specified set, the evaluation result of the discriminator is usually correct, that is, the discriminator can correctly determine whether the image is in the specified set. When the feature difference between the image generated by the generator and the images in the specified set is not obvious, it is difficult for the discriminator to always correctly determine whether the image is in the specified set. In this way, the training stop condition can be set by setting the correct ratio threshold of the discriminant evaluation result, so that the image generated by the generator is more consistent with the features of the training target in the training set.

[0034] Before training the generator, the discriminator can also be pre-trained. For example, random features are input to the generator to obtain an image, and the discriminator compares the features of the image with the animated organs in the training sample. Figure 1 The discriminator is used to evaluate the image generated by the generator, and the parameters in the discriminator are adjusted based on whether the evaluation result is correct, until the discriminator can correctly judge whether the image generated by the generator is consistent with the animated organ diagram in the training sample. After the discriminator is trained, the generator can be trained with the discriminator. It is worth noting that the training of the generator and the discriminator can also be carried out synchronously, so that they can constrain each other, so that the image generated by the generator is more consistent with the characteristics of the animated organ diagram, and the discriminator can evaluate the image more correctly.

[0035] In a possible implementation, the training samples are obtained by determining the position of the organ in each MRI sample image, and generating an animated pronunciation organ corresponding to the position of the organ in each MRI sample image to obtain an animated pronunciation organ map.

[0036] The position of each pronunciation organ can be distinguished by the outline of the color block area in the MRI sample image, and the position of the pronunciation organ can also be identified by a recognition model, or the organ template map can be overlapped with the MRI sample image, and the area in the MRI sample image is merged based on the pronunciation organ position in the organ template map, and the color block in the area where the pronunciation organ is located is used as the position of the organ.

[0037] In a possible implementation, for each MRI sample image, the organ contour of the MRI sample image is extracted, and the organ contour of each pronunciation organ is filled with the pronunciation organ image corresponding to the pronunciation organ.

[0038] The vocal organ image can be a cartoon image or a realistic image. In a possible implementation, an organ map can be called from a preset flash animation library, and the organ map corresponding to the vocal organ can be filled in the organ outline of each vocal organ. It is worth noting that there may be multiple organ maps for the same vocal organ in the flash animation library, and an organ map can be automatically selected for filling, or the type of map can be modified according to the user's specification for filling.

[0039] In a possible implementation, for the MRI sample image corresponding to the first frame of the MRI sample video, an organ map is called from a preset flash animation library, and the organ map corresponding to the pronunciation organ is filled in the organ contour of each pronunciation organ; for the MRI sample images corresponding to other video frames, the organ map corresponding to each pronunciation organ in the MRI sample image corresponding to the first frame is called from the flash animation library to fill in the organ contour corresponding to each pronunciation organ.

[0040] That is to say, after filling the first frame with texture, other frames can be filled based on the texture type of the first frame, so that the texture style for the same organ in all animation frames is the same, making the final animation video more natural.

[0041] For example, there are 3 types of textures for the tongue and 4 types of textures for the teeth in the flash animation library. When filling the MRI sample image corresponding to the first frame, the tongue 1 texture is selected for the tongue and the teeth 3 texture is selected for the teeth to fill the contours of the tongue and the teeth respectively. When filling other subsequent frames, the tongue 1 texture can be automatically selected to fill the contour of the tongue, and the teeth 3 texture can be selected to fill the contour of the teeth.

[0042] Considering that there may be deviations in the extraction of organ contours, in a possible implementation, after the organ contours are extracted, the organ contours may be corrected. The organ contours may be corrected frame by frame, or after the organ contours of the first frame are corrected, the organ contours may be tracked by identifying feature points, thereby achieving correction of the organ contours of other frames.

[0043] In a possible implementation, for the MRI sample image corresponding to the first frame of the MRI sample video, the organ contour in the MRI sample image is adjusted based on the MRI sample image so that the organ contour corresponds to the feature points in the MRI sample image; for the MRI sample images corresponding to other video frames, feature point tracking is performed on the feature points in the MRI sample image and the feature points in the previous video frame of the MRI sample image, and based on the feature point tracking result, the organ contour in the MRI sample image is automatically adjusted.

[0044] Figure 2 FIG. 1 is a schematic diagram of a training process of an animation generation model according to an exemplary disclosed embodiment. Figure 2 As shown, the MRI sample video is divided into frames to obtain MRI sample images, and the organ contours are extracted. After the organ contours are extracted, the organ contours in each frame are adjusted, and the maps corresponding to the organs are extracted from the flash map library to fill the organ contours to obtain animated pronunciation organ maps. The generator in the animation generation model generates training animation images based on the MRI sample images, and calculates the loss value based on the training animation images and the animated pronunciation organ maps and adjusts the parameters in the generator. The discriminator is used to evaluate whether the training animation images generated by the generator belong to the animated pronunciation organ maps in the training samples, until the accuracy of the evaluation results of the discriminator is lower than the success rate threshold, then the training process of the animation generation model is completed.

[0045] Through the above technical solution, at least the following technical effects can be achieved:

[0046] By inputting the MRI video into the animation generation model and outputting a pronunciation organ animation video corresponding to the pronunciation organs in the MRI video based on the MRI video, the MRI video can be converted into an animation video, making the position, shape, state, etc. of the pronunciation organs in the MRI video clearer, thereby improving the efficiency of users in extracting information from the MRI video.

[0047] Figure 3 is a block diagram of an animation generating device according to an exemplary disclosed embodiment. Figure 3 As shown, the device 300 includes:

[0048] The input module 310 is used to input the MRI video to be animated into the animation generation model.

[0049] The generating module 320 is used to output a pronunciation organ animation video corresponding to the pronunciation organ in the MRI video based on the MRI video.

[0050] In a possible implementation, the training samples of the animation generation model include multiple MRI sample images and an animated pronunciation organ map corresponding to each MRI sample image, and the animated pronunciation organ map is obtained in the following manner: determining the position of the pronunciation organ in each MRI sample image; generating an animated pronunciation organ corresponding to the position of the organ at the position of the pronunciation organ in each MRI sample image, thereby obtaining an animated pronunciation organ map.

[0051] In a possible embodiment, the device also includes a frame segmentation module for dividing the MRI video into multiple video frames; the input module is used to input each video frame of the MRI video into the animation generation model; the generation module is used to output pronunciation organ animation frames based on the pronunciation organs in the video frames; and the pronunciation organ animation frames are combined into a pronunciation organ animation video.

[0052] In one possible embodiment, the animation generation model is a generative adversarial network model, which includes a generator for generating animation images based on MRI images; the device also includes a training module, which is used to repeatedly execute the steps of the generator generating training animation images based on the MRI sample images, generating loss values ​​based on the animation pronunciation organ map corresponding to the MRI sample images and a preset loss function, adjusting the parameters in the generator based on the loss value, and evaluating the training animation images based on the animation pronunciation organ map by the discriminator of the generative adversarial network model, until the evaluation result meets the preset evaluation result condition.

[0053] In a possible implementation, the training module is further configured to extract the organ contour of each MRI sample image; and fill the organ contour of each pronunciation organ with the pronunciation organ image corresponding to the pronunciation organ.

[0054] In a possible implementation manner, the training module is further used to call an organ map from a preset flash animation library, and fill the organ map corresponding to the organ in the organ outline of each pronunciation organ.

[0055] In a possible implementation, the MRI sample image is an image obtained by framing an MRI sample video, and the training module is further used to call an organ map from a preset flash animation library for the MRI sample image corresponding to the first frame of the MRI sample video, and fill the organ map corresponding to the pronunciation organ in the organ contour of each pronunciation organ; for the MRI sample images corresponding to other video frames, call the organ map corresponding to each pronunciation organ in the MRI sample image corresponding to the first frame from the flash animation library and fill it in the organ contour corresponding to each pronunciation organ.

[0056] In a possible implementation, the training module is also used to correct the contour of the organ.

[0057] In a possible implementation, the MRI sample image is an image obtained by framing an MRI sample video, and the training module is further used to adjust the organ contour in the MRI sample image based on the MRI sample image corresponding to the first frame of the MRI sample video, so that the organ contour corresponds to the feature points in the MRI sample image; for the MRI sample images corresponding to other video frames, feature point tracking is performed on the feature points in the MRI sample image and the feature points in the previous video frame of the MRI sample image, and based on the feature point tracking result, the organ contour in the MRI sample image is automatically adjusted.

[0058] The specific steps performed by the above modules have been described in detail in the embodiments of the method part and will not be repeated here.

[0059] Through the above technical solution, at least the following technical effects can be achieved:

[0060] By inputting the MRI video into the animation generation model and outputting a pronunciation organ animation video corresponding to the pronunciation organs in the MRI video based on the MRI video, the MRI video can be converted into an animation video, making the position, shape, state, etc. of the pronunciation organs in the MRI video clearer, thereby improving the efficiency of users in extracting information from the MRI video.

[0061] Reference below Figure 4 , which shows a schematic diagram of the structure of an electronic device 400 suitable for implementing the embodiment of the present disclosure. The terminal device in the embodiment of the present disclosure may include but is not limited to mobile terminals such as mobile phones, laptop computers, digital broadcast receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), vehicle-mounted terminals (such as vehicle-mounted navigation terminals), etc., and fixed terminals such as digital TVs, desktop computers, etc. Figure 4The electronic device shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present disclosure.

[0062] like Figure 4 As shown, the electronic device 400 may include a processing device (e.g., a central processing unit, a graphics processing unit, etc.) 401, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 402 or a program loaded from a storage device 408 into a random access memory (RAM) 403. In the RAM 403, various programs and data required for the operation of the electronic device 400 are also stored. The processing device 401, the ROM 402, and the RAM 403 are connected to each other via a bus 404. An input / output (I / O) interface 405 is also connected to the bus 404.

[0063] Typically, the following devices may be connected to the I / O interface 405: an input device 406 including, for example, a touch screen, a touch pad, a keyboard, a mouse, a camera, a microphone, an accelerometer, a gyroscope, etc.; an output device 407 including, for example, a liquid crystal display (LCD), a speaker, a vibrator, etc.; a storage device 408 including, for example, a magnetic tape, a hard disk, etc.; and a communication device 409. The communication device 409 may allow the electronic device 400 to communicate with other devices wirelessly or by wire to exchange data. Although Figure 4 The electronic device 400 is shown with various devices, but it should be understood that it is not required to implement or possess all the devices shown. More or fewer devices may be implemented or possessed instead.

[0064] In particular, according to an embodiment of the present disclosure, the process described above with reference to the flowchart can be implemented as a computer software program. For example, an embodiment of the present disclosure includes a computer program product, which includes a computer program carried on a non-transitory computer-readable medium, and the computer program contains program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from the network through the communication device 409, or installed from the storage device 408, or installed from the ROM 402. When the computer program is executed by the processing device 401, the above-mentioned functions defined in the method of the embodiment of the present disclosure are executed.

[0065] It should be noted that the computer-readable medium disclosed above may be a computer-readable signal medium or a computer-readable storage medium or any combination of the above two. The computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present disclosure, a computer-readable storage medium may be any tangible medium containing or storing a program that may be used by or in combination with an instruction execution system, device or device. In the present disclosure, a computer-readable signal medium may include a data signal propagated in a baseband or as part of a carrier wave, in which a computer-readable program code is carried. This propagated data signal may take a variety of forms, including but not limited to an electromagnetic signal, an optical signal, or any suitable combination of the above. The computer readable signal medium may also be any computer readable medium other than a computer readable storage medium, which may send, propagate or transmit a program for use by or in conjunction with an instruction execution system, apparatus or device. The program code contained on the computer readable medium may be transmitted using any suitable medium, including but not limited to: wires, optical cables, RF (radio frequency), etc., or any suitable combination of the above.

[0066] Computer program code for performing the operations of the present disclosure may be written in one or more programming languages ​​or a combination thereof, including, but not limited to, object-oriented programming languages, such as Java, Smalltalk, C++, and conventional procedural programming languages, such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0067] The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, function and operation of the system, method and computer program product according to various embodiments of the present disclosure. In this regard, each square box in the flow chart or block diagram can represent a module, a program segment or a part of a code, and the module, the program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some implementations as replacements, the functions marked in the square box can also occur in a sequence different from that marked in the accompanying drawings. For example, two square boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each square box in the block diagram and / or flow chart, and the combination of the square boxes in the block diagram and / or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0068] The modules involved in the embodiments described in the present disclosure may be implemented by software or hardware, wherein the name of a module does not, in some cases, limit the module itself.

[0069] The functions described above herein may be performed at least in part by one or more hardware logic components. For example, without limitation, exemplary types of hardware logic components that may be used include: field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), complex programmable logic devices (CPLDs), and the like.

[0070] In the context of the present disclosure, a machine-readable medium may be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, device, or equipment. A machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or equipment, or any suitable combination of the foregoing. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0071] According to one or more embodiments of the present disclosure, Example 1 provides an animation generation method, comprising: inputting a magnetic resonance imaging (MRI) video to be animated into an animation generation model; and based on the MRI video, outputting a pronunciation organ animation video corresponding to the pronunciation organs in the MRI video.

[0072] According to one or more embodiments of the present disclosure, Example 2 provides the method of Example 1, wherein the training samples of the animation generation model include multiple MRI sample images and an animated pronunciation organ map corresponding to each MRI sample image, and the animated pronunciation organ map is obtained in the following manner: determining the position of the pronunciation organ in each MRI sample image; based on the position of the pronunciation organ in each MRI sample image, an animated pronunciation organ corresponding to the position of the organ is obtained, thereby obtaining an animated pronunciation organ map.

[0073] According to one or more embodiments of the present disclosure, Example 3 provides the method of Example 2, which also includes: dividing the MRI video into multiple video frames; inputting the magnetic resonance imaging MRI video to be animated into the animation generation model, including: inputting each video frame of the MRI video into the animation generation model; outputting a pronunciation organ animation video corresponding to the pronunciation organ in the MRI video based on the MRI video, including: pronunciation organ animation frames output based on the pronunciation organs in the video frames; and combining the pronunciation organ animation frames into a pronunciation organ animation video.

[0074] According to one or more embodiments of the present disclosure, Example 4 provides the method of Example 2, wherein the animation generation model is an adversarial generative network model, and the animation generation model includes a generator for generating animation images based on MRI images; the animation generation model is trained in the following manner: repeatedly executing the steps of the generator generating training animation images based on the MRI sample images, generating loss values ​​based on the animation pronunciation organ map corresponding to the MRI sample images and a preset loss function, adjusting the parameters in the generator based on the loss values, and the discriminator of the adversarial generative network model evaluating the training animation images based on the animation pronunciation organ map, until the evaluation result meets the preset evaluation result conditions.

[0075] According to one or more embodiments of the present disclosure, Example 5 provides the method of Example 2, wherein determining the position of the organ in each MRI sample image includes: for each MRI sample image, extracting the organ contour of the MRI sample image; generating an animated pronunciation organ corresponding to the position of the pronunciation organ in each MRI sample image, including: filling the pronunciation organ image corresponding to the pronunciation organ in the organ contour of each pronunciation organ.

[0076] According to one or more embodiments of the present disclosure, Example 6 provides the method of Example 5, wherein filling the organ contour of each pronunciation organ with a color corresponding to the pronunciation organ comprises: calling an organ map from a preset flash animation library, and filling the organ contour of each pronunciation organ with the organ map corresponding to the organ.

[0077] According to one or more embodiments of the present disclosure, Example 7 provides the method of Example 6, wherein the MRI sample image is an image obtained by framing the MRI sample video, and the calling of the organ map from the preset flash animation library and filling the organ contour of each pronunciation organ with the organ map corresponding to the pronunciation organ also includes: for the MRI sample image corresponding to the first frame of the MRI sample video, calling the organ map from the preset flash animation library and filling the organ contour of each pronunciation organ with the organ map corresponding to the pronunciation organ; for the MRI sample images corresponding to other video frames, calling the organ map corresponding to each pronunciation organ in the MRI sample image corresponding to the first frame from the flash animation library and filling the organ contour corresponding to each pronunciation organ.

[0078] According to one or more embodiments of the present disclosure, Example 8 provides the method of Example 5, wherein after extracting the organ contour of the MRI sample image, the method further includes: correcting the organ contour.

[0079] According to one or more embodiments of the present disclosure, Example 9 provides the method of Example 8, wherein the MRI sample image is an image obtained by framing an MRI sample video, and the correction of the organ contour includes: for the MRI sample image corresponding to the first frame of the MRI sample video, adjusting the organ contour in the MRI sample image based on the MRI sample image so that the organ contour corresponds to the feature points in the MRI sample image; for the MRI sample images corresponding to other video frames, performing feature point tracking on the feature points in the MRI sample image and the feature points in the previous video frame of the MRI sample image, and automatically adjusting the organ contour in the MRI sample image based on the feature point tracking result.

[0080] According to one or more embodiments of the present disclosure, Example 10 provides an animation generation device, comprising: an input module for inputting a magnetic resonance imaging (MRI) video to be animated into an animation generation model; and an acquisition module for acquiring an animation video of a pronunciation organ output by the animation generation model based on the pronunciation organs in the MRI video.

[0081] According to one or more embodiments of the present disclosure, Example 11 provides the device of Example 10, wherein the training samples of the animation generation model include multiple MRI sample images and an animated pronunciation organ map corresponding to each MRI sample image, and the animated pronunciation organ map is obtained in the following manner: determining the position of the pronunciation organ in each MRI sample image; at the position of the pronunciation organ in each MRI sample image, generating an animated pronunciation organ corresponding to the position of the organ, thereby obtaining an animated pronunciation organ map.

[0082] According to one or more embodiments of the present disclosure, Example 12 provides the device of Example 11, which also includes a frame segmentation module for dividing the MRI video into multiple video frames; the input module is used to input each video frame of the MRI video into the animation generation model; the generation module is used to output pronunciation organ animation frames based on the pronunciation organs in the video frames; and the pronunciation organ animation frames are combined into a pronunciation organ animation video.

[0083] According to one or more embodiments of the present disclosure, Example 13 provides the device of Example 11, wherein the animation generation model is an adversarial generative network model, and the animation generation model includes a generator for generating animation images based on MRI images; the device also includes a training module for repeatedly executing the steps of the generator generating training animation images based on the MRI sample images, generating loss values ​​based on the animation pronunciation organ map corresponding to the MRI sample images and a preset loss function, adjusting the parameters in the generator based on the loss values, and evaluating the training animation images based on the animation pronunciation organ map by the discriminator of the adversarial generative network model, until the evaluation result meets the preset evaluation result conditions.

[0084] According to one or more embodiments of the present disclosure, Example 14 provides the device of Example 11, wherein the training module is further used to extract the organ contour of each MRI sample image; and fill the organ contour of each pronunciation organ with the pronunciation organ image corresponding to the pronunciation organ.

[0085] According to one or more embodiments of the present disclosure, Example 15 provides the apparatus of Example 14, wherein the training module is further used to call an organ map from a preset flash animation library, and fill the organ map corresponding to the organ in the organ contour of each pronunciation organ.

[0086] According to one or more embodiments of the present disclosure, Example 16 provides the device of Example 16, wherein the MRI sample image is an image obtained by framing an MRI sample video, and the training module is further used to call an organ map from a preset flash animation library for the MRI sample image corresponding to the first frame of the MRI sample video, and fill the organ map corresponding to the pronunciation organ in the organ contour of each pronunciation organ; for the MRI sample images corresponding to other video frames, call the organ map corresponding to each pronunciation organ in the MRI sample image corresponding to the first frame from the flash animation library and fill it in the organ contour corresponding to each pronunciation organ.

[0087] According to one or more embodiments of the present disclosure, Example 17 provides the apparatus of Example 16, wherein the training module is further used to correct the contour of the organ.

[0088] According to one or more embodiments of the present disclosure, Example 18 provides the device of Example 17, wherein the MRI sample image is an image obtained by framing an MRI sample video, and the training module is further used to adjust the organ contour in the MRI sample image based on the MRI sample image corresponding to the first frame of the MRI sample video, so that the organ contour corresponds to the feature points in the MRI sample image; for the MRI sample images corresponding to other video frames, feature point tracking is performed on the feature points in the MRI sample image and the feature points in the previous video frame of the MRI sample image, and based on the feature point tracking result, the organ contour in the MRI sample image is automatically adjusted.

[0089] The above description is only a preferred embodiment of the present disclosure and an explanation of the technical principles used. Those skilled in the art should understand that the scope of disclosure involved in the present disclosure is not limited to the technical solutions formed by a specific combination of the above technical features, but should also cover other technical solutions formed by any combination of the above technical features or their equivalent features without departing from the above disclosed concept. For example, the above features are replaced with the technical features with similar functions disclosed in the present disclosure (but not limited to) by each other to form a technical solution.

[0090] In addition, although each operation is described in a specific order, this should not be understood as requiring these operations to be performed in the specific order shown or in a sequential order. Under certain circumstances, multitasking and parallel processing may be advantageous. Similarly, although some specific implementation details are included in the above discussion, these should not be interpreted as limiting the scope of the present disclosure. Some features described in the context of a separate embodiment can also be implemented in a single embodiment in combination. On the contrary, the various features described in the context of a single embodiment can also be implemented in multiple embodiments individually or in any suitable sub-combination mode.

[0091] Although the subject matter has been described in language specific to structural features and / or method logic actions, it should be understood that the subject matter defined in the appended claims is not necessarily limited to the specific features or actions described above. On the contrary, the specific features and actions described above are merely example forms of implementing the claims. Regarding the device in the above embodiment, the specific manner in which each module performs the operation has been described in detail in the embodiment related to the method, and will not be elaborated here.

Claims

1. An animation generation method, characterized in that: The method comprises: Dividing a magnetic resonance imaging (MRI) video to be animated into a plurality of video frames; Inputting each video frame of the MRI video into an animation generation model, wherein the training samples of the animation generation model include a plurality of MRI sample images and an animated pronunciation organ map corresponding to each MRI sample image, each animated pronunciation organ map includes a pronunciation organ, and the organ contour of the pronunciation organ is filled with a pronunciation organ image corresponding to the pronunciation organ; Obtaining animation frames of pronunciation organs output by the animation generation model based on pronunciation organs in each video frame of the MRI video; The vocal organ animation frames are combined into a vocal organ animation video.

2. The method according to claim 1, characterized in that The animated articulatory organ diagram is obtained in the following manner: determining the location of the vocal organ in each MRI sample image; At the position of the vocal organ in each MRI sample image, an animated vocal organ corresponding to the position of the organ is generated, thereby obtaining an animated vocal organ map.

3. The method according to claim 2, characterized in that The animation generation model is a generative adversarial network model, and the animation generation model includes a generator for generating animation images based on MRI images; The animation generation model is trained in the following way: Repeat the steps of generating a training animation image based on the MRI sample image by the generator, generating a loss value based on the animation pronunciation organ map corresponding to the MRI sample image and a preset loss function, adjusting the parameters in the generator based on the loss value, and evaluating the training animation image based on the animation pronunciation organ map by the discriminator of the generative adversarial network model, until the evaluation result meets the preset evaluation result condition.

4. The method according to claim 2, characterized in that: Determining the position of the organ in each MRI sample image includes: For each MRI sample image, extracting the organ contour of the MRI sample image; The position of the pronunciation organ in each MRI sample image, and generating an animation pronunciation organ corresponding to the position of the pronunciation organ, include: The organ contour of each vocal organ is filled with a vocal organ image corresponding to the vocal organ.

5. The method according to claim 4, characterized in that The step of filling the organ contour of each pronunciation organ with the pronunciation organ image corresponding to the pronunciation organ comprises: The organ map is called from the preset flash animation library, and the organ map corresponding to the organ is filled in the organ outline of each vocal organ.

6. The method according to claim 5, characterized in that The MRI sample image is an image obtained by dividing the MRI sample video into frames, calling the organ map from the preset flash animation library, and filling the organ map corresponding to the pronunciation organ in the organ outline of each pronunciation organ, further comprising: For the MRI sample image corresponding to the first frame of the MRI sample video, an organ map is called from a preset flash animation library, and the organ map corresponding to the pronunciation organ is filled in the organ outline of each pronunciation organ; For the MRI sample images corresponding to other video frames, the organ maps corresponding to the various pronunciation organs in the MRI sample images corresponding to the first frame are called from the flash animation library to fill in the organ contours corresponding to the various pronunciation organs.

7. The method according to claim 4, characterized in that After extracting the organ contour of the MRI sample image, the method further includes: The contour of the organ is corrected.

8. The method according to claim 7, characterized in that The MRI sample image is an image obtained by dividing the MRI sample video into frames, and the correcting of the organ contour includes: For the MRI sample image corresponding to the first frame of the MRI sample video, adjusting the organ contour in the MRI sample image based on the MRI sample image so that the organ contour corresponds to the feature point in the MRI sample image; For the MRI sample images corresponding to other video frames, feature point tracking is performed on feature points in the MRI sample image and feature points in a previous video frame of the MRI sample image, and based on the feature point tracking result, the organ contour in the MRI sample image is automatically adjusted.

9. An animation generating device, characterized in that: The device comprises: An input module is used to divide a magnetic resonance imaging (MRI) video to be animated and divide it into a plurality of video frames; each video frame of the MRI video is input into an animation generation model, wherein the training samples of the animation generation model include a plurality of MRI sample images and animation pronunciation organ maps corresponding to each MRI sample image, each animation pronunciation organ map includes a pronunciation organ, and the organ contour of the pronunciation organ is filled with a pronunciation organ image corresponding to the pronunciation organ; The acquisition module is used to acquire the pronunciation organ animation frames output by the pronunciation organs in each video frame of the MRI video based on the animation generation model, and combine the pronunciation organ animation frames into a pronunciation organ animation video.

10. A computer readable medium having a computer program stored thereon, characterized in that: When the program is executed by a processing device, the steps of the method described in any one of claims 1 to 8 are implemented.

11. An electronic device, characterized in that: include: a storage device having a computer program stored thereon; A processing device, configured to execute the computer program in the storage device to implement the steps of the method according to any one of claims 1 to 8.

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