Reinforcement Learning for Animation Character Bone Training
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Solution Overview
Problem
Current techniques for creating animated characters in video games are time-consuming and computationally expensive, as they often rely on reusing a single skeleton or employing motion retargeting, which can fail when characters have vastly different structures.
Innovation Solution
The use of reinforcement learning to train animation characters by mapping disparate target animation data to specific parts of a bone hierarchy, allowing for the blending of diverse motion sequences within a simulation environment, thereby creating realistic movements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If motion retargeting is used to transform motion to a new character, then the motion can be customized to the new character, but the process is still time consuming and computationally expensive
Solution Approach 1:
The patent divides the character skeleton into multiple bone hierarchies (e.g., left arm, right arm, left leg, right leg, torso) and trains each bone hierarchy separately using reinforcement learning. This segmentation allows parallel training of different body parts, significantly reducing overall training time while maintaining customized motion for each character.
Solution Approach 2:
The patent pre-trains bone hierarchies using reinforcement learning to learn optimal motion policies before actual animation generation. This preliminary training phase allows the system to quickly generate customized motions for new characters without performing time-consuming iterative joint manipulation at runtime.
2Adaptability or versatility
If motion retargeting uses joint manipulation to generate transformed motion, then the motion can be customized, but the process is computationally expensive
Solution Approach 1:
The patent replaces the traditional mechanical joint manipulation approach with reinforcement learning-based policy networks. Instead of iteratively adjusting joints through computational optimization, the pre-trained neural networks directly predict bone hierarchy transformations, dramatically reducing computational resource requirements while maintaining motion customization capability.
Solution Approach 2:
The computationally intensive training process is performed in advance during an offline phase, creating pre-trained policy networks. During actual animation generation, the system only needs to execute the pre-trained networks, which requires minimal computational resources compared to real-time joint manipulation.
3Productivity
If a single skeleton is reused for multiple characters, then character creation time is reduced, but all characters will look alike and have the same movements
Solution Approach 1:
The patent segments the character skeleton into multiple independent bone hierarchies that can be independently trained and transformed. This allows the same base skeleton to generate diverse character movements by applying different transformations to each bone hierarchy, achieving character diversity while maintaining efficient character creation.
Solution Approach 2:
The patent applies different motion transformations to different parts of the skeleton (e.g., different arm movements, leg movements, torso movements) based on character-specific parameters. This local customization allows characters to have unique movement patterns while sharing the same overall skeleton structure, achieving both efficiency and diversity.
4Adaptability or versatility
If motion retargeting is used to transform motion, then motion can be adapted to new characters, but it may fail when the new character is vastly different than the base character
Solution Approach 1:
The patent uses reinforcement learning to learn robust motion policies that can adapt to various character parameters and proportions. The trained policy networks generalize well to characters with vastly different structures from the training data, ensuring reliable motion transformation even when the new character differs significantly from the base character.
Solution Approach 2:
The patent creates universal policy networks through reinforcement learning that can handle multiple character types and motion scenarios. These trained networks are not limited to specific character proportions or motion styles, making the motion adaptation reliable across diverse character designs and movement patterns.
Data Source
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AI summary
A method for training an animation character, including mapping first animation data defining a first motion sequence to a first subset of bones of a trained character, and mapping second animation data defining a second motion sequence to a second subset of bones. A bone hierarchy includes the first subset of bones and second subset of bones. Reinforcement learning is applied iteratively for training the first subset of bones using the first animation data and for training the second subset of bones using the second animation data. Training of each subset of bones is performed concurrently at each iteration. Training includes adjusting orientations of bones. The first subset of bones is composited with the second subset of bones at each iteration by applying physics parameters of a simulation environment to the adjusted orientations of bones in the first and second subset of bones.