AI technology-based animation production large model method
Through the animation production big model method based on AI technology, deep learning technology is used to automate the animation production process, and the problems of inefficient, high cost and limited creativity of traditional animation production are solved, and efficient, low-cost and creative animation production is achieved.
Patent Information
- Application Number
- CN202411205499.X
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-08-30
- Publication Date
- 2024-12-24
AI Technical Summary
Traditional animation production is inefficient, costly and limited in creativity, making it difficult to adapt to rapidly changing market demand.
Using AI-based animation production large-scale model method, through data collection and preprocessing, character design, scene construction, action generation and special effects addition model training, use deep learning technology to build convolutional neural networks and generative adversarial networks, automation and intelligence Anime production process.
Significantly improve the efficiency of animation production, reduce costs, expand creativity, provide personalized customization and scalability, and adapt to market demand.
Abstract
Description
Technical Field
[0001] The present invention relates to the field of artificial intelligence technology, and in particular to a method for making large animation models based on AI technology. Background Art
[0002] Traditional animation production usually relies on a lot of manpower and time, including character design, scene construction, action generation, special effects addition and other links. These links are not only labor-intensive but also inefficient. With the continuous development of artificial intelligence technology, especially the widespread application of deep learning in the field of image processing and generation, it has become possible to use AI technology to solve problems in animation production. Summary of the invention
[0003] The technical problems to be solved by the present invention are: 1) Low efficiency: Traditional animation production requires a lot of manpower and time, especially in the links of character design and scene construction, which are inefficient and difficult to adapt to the rapidly changing market demand;
[0004] 2) High cost: Due to the large amount of manpower and time required, the cost of traditional animation production is relatively high, which is unaffordable for small-scale production teams or individual creators;
[0005] 3) Limited creativity: In the traditional animation production process, character design, scene construction, etc. are often limited by the creator's personal ability and experience, making it difficult to generate new creativity and ideas.
[0006] In order to solve the above technical problems, the present invention provides a technical solution: a method for making a large model of animation based on AI technology, comprising the following steps:
[0007] S1, data collection and preprocessing;
[0008] S1.2, Data Collection: Collect a large amount of animation images, videos, audio data and related text descriptions and sketches;
[0009] S1.3, Data cleaning: remove noisy, duplicate or low-quality data;
[0010] S1.4, Data augmentation: Increase the diversity of the dataset by rotating, scaling, and cropping;
[0011] S1.5, Data annotation: Annotate image and video data with key frames, roles, scenes, and actions for subsequent model training;
[0012] S2, character design model training;
[0013] S2.1. Use deep learning technology to build a character design model through convolutional neural networks and generative adversarial networks;
[0014] S2.2, use the annotated character images and corresponding text descriptions or sketches as training data;
[0015] S2.3. Through supervised learning, the model learns the mapping relationship from text or sketch to character image;
[0016] S2.4. Continuously iterate and optimize the model to improve the accuracy and diversity of generated characters;
[0017] S3, scene construction model training;
[0018] S3.1, also use deep learning technology to build a scene construction model;
[0019] S3.2, using annotated scene images and corresponding text descriptions or sketches as training data;
[0020] S3.3. Through learning, the model can understand the scene elements and layout described in text or sketches;
[0021] S3.4, after the training is completed, the model generates an animation scene that meets the requirements according to the user input;
[0022] S4, action generation model training;
[0023] S4.1. Construct an action generation model using time series data and skeleton key point detection technology;
[0024] S4.2, collecting video data containing character actions, and extracting key frames and skeleton key point information;
[0025] S4.3. Use this information as training data to allow the model to learn the ability to generate continuous action sequences from action descriptions or example videos;
[0026] S4.4. Improve the fluency and naturalness of generated actions by optimizing the model;
[0027] S5, special effects adding model training;
[0028] S5.1. Build a special effect adding model for adding special effects to animation images or videos;
[0029] S5.2. Collect animation images and video data containing various special effects as training sets;
[0030] S5.3. Use deep learning technology to learn the generation method and parameter settings of special effects;
[0031] S5.4. After training is completed, the model can automatically add special effects according to the special effect type and parameter settings selected by the user;
[0032] S.6, animation synthesis and output;
[0033] S.6.1. Synthesize characters, scenes, actions and special effects to generate complete animation works;
[0034] S.6.2. Adjust synthesis parameters and effects according to user needs and settings;
[0035] S.6.3. Output the synthesized animation work as a video file or other format.
[0036] Compared with the prior art, the advantages of the present invention are: 1) Improved efficiency: Through automation and intelligent means, the efficiency of animation production is greatly improved, and the investment of manpower and time is reduced;
[0037] 2) Reduce costs: Due to the reduction of manpower and time investment, the cost of animation production is reduced, making it affordable for more small-scale production teams or individual creators;
[0038] 3) Expanding creativity: Through the training and learning of deep learning models, more creativity and ideas can be generated, breaking through the creative limitations in traditional animation production;
[0039] 4) Strong flexibility: This method can be customized according to different needs of users to generate animation works that meet user requirements;
[0040] 5) Good scalability: With the continuous advancement of technology and the continuous accumulation of data, this method can be further expanded and optimized to improve the quality and efficiency of generating animation works.
[0041] Furthermore, the data collection and preprocessing steps are used to collect a large amount of image, video, and audio data required for animation production, and to clean, enhance, and annotate the data to improve data quality and model training effects.
[0042] Furthermore, the character design model training step automatically generates an anime character image that meets the requirements based on the text description or sketch input by the user.
[0043] Furthermore, the scene construction model training step automatically generates an animation scene that meets the requirements based on the text description or sketch input by the user.
[0044] Furthermore, the action generation model is trained to automatically generate a continuous action sequence of a character based on the action description or sample video input by the user.
[0045] Furthermore, the special effects adding model training step adds special effects to the animation images or videos, including light and shadow, smoke, and fire effects.
[0046] Furthermore, the animation synthesis and output step synthesizes the characters, scenes, actions and special effects to generate a complete animation work, and outputs it as a video file or other format for subsequent release or dissemination. DETAILED DESCRIPTION
[0047] In the specific implementation of the present invention, a method for making a large model of animation based on AI technology is proposed, which specifically includes the following steps:
[0048] S1. Data collection and preprocessing: Collect a large amount of animation-related images, videos, audio and other data, and clean, enhance and annotate them to improve data quality and model training effects.
[0049] S2. Character design model training: Use deep learning technology to build a character design model, and train the model through supervised learning so that it can automatically generate anime character images that meet the requirements based on the text description or sketch input by the user.
[0050] S3. Scene construction model training: Deep learning technology is also used to build a scene construction model. By learning the scene elements and layout described in text or sketches, an animation scene that meets the requirements is generated.
[0051] S4. Action generation model training: Use time series data and skeleton key point detection technology to build an action generation model. By learning the ability to generate continuous action sequences from action descriptions or example videos, the fluency and naturalness of generated actions can be improved.
[0052] S5. Special effects adding model training: Build a special effects adding model to add special effects, such as light, shadow, smoke, flame, etc., to animation images or videos to enhance the visual effects of animation works.
[0053] S6. Animation synthesis and output: Synthesize characters, scenes, actions and special effects to generate complete animation works, and output them as video files or other formats.
[0054] The principles of the data collection and preprocessing steps include:
[0055] 1) Data collection: Obtain animation-related multimedia data through web crawlers, public datasets, and cooperative institutions.
[0056] 2) Data cleaning: Use image processing technology to remove noisy, blurred, repeated or low-quality images; for video data, perform de-jittering and stabilization processing.
[0057] 3) Data enhancement: Use image transformation techniques such as rotation, scaling, translation, flipping, as well as color space adjustment, brightness and contrast changes to increase the diversity and scale of the data set.
[0058] 4) Data annotation: Use professional annotation tools to annotate images and videos in detail, including key frames, characters, scenes, actions, etc., and generate annotation files as input for model training.
[0059] The principles of the character design model training steps include:
[0060] 1) Model construction: Use deep learning techniques, such as convolutional neural networks (CNN) and generative adversarial networks (GAN), to build character design models.
[0061] 2) Supervised learning: Use labeled character images and corresponding text descriptions or sketches as training data to train the model through supervised learning.
[0062] 3) Feature extraction: The model learns to extract key features such as shape, color, texture, etc. from text or sketches and maps them to the character image.
[0063] 4) Generation optimization: Improve the accuracy and diversity of generated characters by continuously iterating and optimizing model parameters.
[0064] The principles of the scenario building model training steps include:
[0065] 1) Model structure: Similar to the character design model, the scene construction model is constructed using deep learning technology.
[0066] 2) Training data: Use annotated scene images and corresponding text descriptions or sketches as training data.
[0067] 3) Scene understanding: The model learns to understand scene elements, layout, lighting and other details described in text or sketches, and maps them to scene images.
[0068] 4) Generation capability: Through training, the model can generate diverse animation scenes that meet user requirements.
[0069] The principles of the action generation model training steps include:
[0070] 1) Time series analysis: Use time series data and skeleton key point detection technology to analyze the motion trajectory and posture changes of the character's actions.
[0071] 2) Model construction: Use deep learning models such as recurrent neural networks (RNN) or long short-term memory networks (LSTM) to build action generation models.
[0072] 3) Action transfer: Learn to transfer actions from example videos to the target character and generate action sequences that meet the requirements.
[0073] 4) Optimization and adjustment: Improve the fluency and naturalness of generated actions by optimizing model parameters and training strategies.
[0074] The principles of the special effects adding model training steps include:
[0075] 1) Special effects analysis: Collect animation images and video data containing various special effects, and analyze the generation methods and parameter settings of special effects.
[0076] 2) Model construction: Use deep learning technology to build a special effects adding model and learn the generation rules and effects of special effects.
[0077] 3) Special effects generation: Based on the special effects type and parameter settings selected by the user, the model automatically adds special effects to the animation image or video.
[0078] 4) Effect optimization: Optimize the generation effect and performance of special effects by adjusting model parameters and training data.
[0079] The principles of animation synthesis and output steps include:
[0080] 1) Synthesis technology: Use computer graphics and image processing technology to accurately synthesize characters, scenes, actions and special effects.
[0081] 2) Parameter adjustment: Adjust synthesis parameters and effects such as color, brightness, contrast, etc. according to user needs and settings.
[0082] 3) Output format: Convert the synthesized animation work into a standard video file format (such as MP4, AVI, etc.), or output it to other formats (such as GIF, PNG, etc.) as required.
[0083] 4) Quality assessment: Perform quality assessment on the synthesized work before output to ensure that it meets user requirements.
[0084] The above shows and describes the basic principles and main features of the present invention and the advantages of the invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The above embodiments and descriptions are only for explaining the principles of the present invention. Without departing from the spirit and scope of the present invention, the present invention may have various changes and improvements, which fall within the scope of the present invention to be protected. The scope of protection of the present invention is defined by the attached claims and their equivalents.
Claims
1. A method for making large animation models based on AI technology, characterized in that The following steps are involved: S1, data collection and preprocessing; S1.2, Data Collection: Collect a large amount of animation images, videos, audio data and related text descriptions and sketches; S1.3, Data cleaning: remove noisy, duplicate or low-quality data; S1.4, Data augmentation: Increase the diversity of the dataset by rotating, scaling, and cropping; S1.5, Data annotation: Annotate image and video data with key frames, roles, scenes, and actions for subsequent model training; S2, character design model training; S2.
1. Use deep learning technology to build a character design model through convolutional neural networks and generative adversarial networks; S2.2, use the annotated character images and corresponding text descriptions or sketches as training data; S2.
3. Through supervised learning, the model learns the mapping relationship from text or sketch to character image; S2.
4. Continuously iterate and optimize the model to improve the accuracy and diversity of generated characters; S3, scene construction model training; S3.1, also use deep learning technology to build a scene construction model; S3.2, using annotated scene images and corresponding text descriptions or sketches as training data; S3.
3. Through learning, the model can understand the scene elements and layout described in text or sketches; S3.4, after the training is completed, the model generates an animation scene that meets the requirements according to the user input; S4, action generation model training; S4.
1. Construct an action generation model using time series data and skeleton key point detection technology; S4.2, collecting video data containing character actions, and extracting key frames and skeleton key point information; S4.
3. Use this information as training data to allow the model to learn the ability to generate continuous action sequences from action descriptions or example videos; S4.
4. Improve the fluency and naturalness of generated actions by optimizing the model; S5, special effects adding model training; S5.
1. Build a special effect adding model for adding special effects to animation images or videos; S5.
2. Collect animation images and video data containing various special effects as training sets; S5.
3. Use deep learning technology to learn the generation method and parameter settings of special effects; S5.
4. After training is completed, the model can automatically add special effects according to the special effect type and parameter settings selected by the user; S.6, animation synthesis and output; S.6.
1. Synthesize characters, scenes, actions and special effects to generate complete animation works; S.6.
2. Adjust synthesis parameters and effects according to user needs and settings; S.6.
3. Output the synthesized animation work as a video file or other format.
2. The method for making large animation models based on AI technology according to claim 1, characterized in that: The data collection and preprocessing steps are used to collect a large amount of image, video, and audio data required for animation production, and to clean, enhance, and annotate the data to improve data quality and model training effects.
3. The method for making large animation models based on AI technology according to claim 1 is characterized in that: The character design model training step automatically generates an animated character image that meets the requirements based on the text description or sketch input by the user.
4. The method for making large animation models based on AI technology according to claim 1 is characterized in that: The scene construction model training step automatically generates an animation scene that meets the requirements based on the text description or sketch input by the user.
5. The method for making large animation models based on AI technology according to claim 1, characterized in that: The action generation model training automatically generates a continuous action sequence of a character according to an action description or a sample video input by a user.
6. The method for making large animation models based on AI technology according to claim 1, characterized in that: The special effects adding model training step adds special effects to the animation image or video, including light and shadow, smoke, and flame effects.
7. The method for making large animation models based on AI technology according to claim 1 is characterized in that: The animation synthesis and output step synthesizes characters, scenes, actions and special effects to generate a complete animation work, and outputs it as a video file or other format for subsequent release or dissemination.