Physics-Guided Motion Diffusion Model for Plausible Animation
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Solution Overview
Problem
Conventional human motion synthesis techniques using denoising diffusion models generate physically implausible motions due to the lack of explicit mechanisms to enforce physical constraints, resulting in artifacts like floating and ground penetration, which limit their application in real-world scenarios such as animation and virtual reality.
Innovation Solution
A physics-guided motion diffusion model is introduced, which incorporates physical constraints into the diffusion process by using a physics-based motion projection module to project denoised motion into a physically plausible space, iteratively refining the motion to reduce artifacts such as floating and ground penetration.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional denoising diffusion models are used for motion synthesis, then the ability to model the distribution of human motion is improved, but physical plausibility deteriorates due to lack of explicit physical constraints
Solution Approach 1:
A physics-based motion projection module is introduced as an intermediary between the diffusion model and the final motion output. This module projects the denoised motion onto a physically plausible manifold by enforcing contact constraints and collision detection, thereby mediating between the diffusion model's distribution modeling capability and the requirement for physical plausibility
Solution Approach 2:
The system implements feedback by iteratively refining the generated motion through multiple passes of the physics-based projection module. The physically plausible motion from one iteration becomes the input for the next iteration, continuously improving physical consistency while preserving the motion distribution characteristics learned by the diffusion model
2Adaptability or versatility
If diffusion models generate motion without physical constraints, then generation flexibility is improved, but artifact generation worsens (floating, foot sliding, ground penetration)
Solution Approach 1:
The physics-based motion projection module performs preliminary anti-action by preemptively correcting potential physical artifacts before they manifest in the final output. Contact constraints and collision detection are applied in advance to prevent floating, foot sliding, and ground penetration artifacts while maintaining the flexibility of the diffusion-based generation process
3Manufacturing precision
If physical constraints are incorporated into the diffusion process, then physical plausibility is improved, but computational complexity increases
Solution Approach 1:
The system segments the motion synthesis process into distinct modules: the diffusion model for distribution modeling and the physics-based projection module for physical constraint enforcement. This segmentation allows each component to specialize in its strength while reducing overall computational complexity compared to a monolithic approach
Data Source
AI summary
Systems and methods are disclosed that improve performance of synthesized motion generated by a diffusion neural network model. A physics-guided motion diffusion model incorporates physical constraints into the diffusion process to model the complex dynamics induced by forces and contact. Specifically, a physics-based motion projection module uses motion imitation in a physics simulator to project the denoised motion of a diffusion step to a physically plausible motion. The projected motion is further used in the next diffusion iteration to guide the denoising diffusion process. The use of physical constraints in the physics-guided motion diffusion model iteratively pulls the motion toward a physically-plausible space, reducing artifacts such as floating, foot sliding, and ground penetration.