AI Motion Binding for Clothing Bones in Character Animation
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Solution Overview
Problem
Existing methods for establishing a binding relationship between motion points of clothes and bones in animations are inefficient and inaccurate due to reliance on artist experience, leading to low efficiency and varying accuracy levels.
Innovation Solution
A method and apparatus using machine learning, specifically deep learning, to automatically generate motion binding parameters between motion points and base bones by performing feature embedding on reference positions to create a global prediction feature, enabling accurate and efficient skinning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If artists manually establish binding relationships between motion points and bones based on experience, then flexibility in handling complex cases is maintained, but efficiency is low and accuracy varies between artists
Solution Approach 1:
The patent replaces the manual mechanical process of artist-based binding relationship establishment with an automated AI system. The system automatically determines motion binding parameters by processing motion capture data and calculating spatial relationships between motion points and bones, eliminating the need for manual artist intervention while maintaining high accuracy and consistency.
Solution Approach 2:
The system performs self-service by automatically analyzing motion capture data, extracting features, and determining binding parameters without requiring external artist input. The AI model processes the data independently, using computational algorithms to establish the binding relationships that would otherwise require manual expertise.
2Measurement precision
If more motion points are processed to improve accuracy, then binding relationship accuracy improves, but processing time and computational complexity increase
Solution Approach 1:
The patent extracts key features from the motion capture data, such as position coordinates, motion trajectories, and spatial relationships. By focusing on these essential features rather than processing all raw data points, the system achieves high accuracy in determining binding parameters while significantly reducing processing time and computational requirements.
Solution Approach 2:
The system applies different processing qualities to different parts of the motion data based on their importance. Critical motion points with significant binding relationships receive more detailed analysis, while less important points are processed more lightly, optimizing the balance between accuracy and processing speed.
Data Source
AI summary
Motion data processing techniques are described herein. The motion data processing technique may include identifying N motion points of a second object model each corresponding to one or more base bones in M base bones, and converting first reference positions of the N motion points and second reference positions of the M bones, to generate a global prediction feature. The global prediction feature may be configured for reflecting a global position feature between the N motion points and corresponding base bones, position features of the M base bones, and structural features of bone chains in which the base bones corresponding to the motion points are located. The techniques may further include predicting a motion binding parameter between each motion point and the corresponding base bone based on the global prediction feature, where any motion point is configured for moving with a corresponding base bone according to a motion binding parameter between the motion point and the corresponding base bone. This can improve efficiency and accuracy of obtaining a motion binding parameter between a motion point and a corresponding base bone.