Learning-based Self-Collision Avoidance in Retargeting using Body Part-specific Signed Distance Fields

Abstract

Motion retargeting is a technique for applying the motion of one character to a new character. Differences in shapes and proportions between characters can cause self-collisions during the retargeting process. To address this issue, we propose a new collision resolution strategy comprising three key components: a collision detection module, a self-collision resolution model, and a training strategy for the collision resolution model. The collision detection module generates collision information based on changes in posture. The self-collision resolution model, which is based on a neural network, uses this collision information to resolve self-collisions. The proposed training strategy enhances the performance of the self-collision resolution model. Compared to previous studies, our self-collision resolution process demonstrates superior performance in terms of accuracy and generalization. Our model reduces the average penetration depth across the entire body by 56%, which is 28% better than the previous studies. Additionally, the minimum distance from the end-effectors to the skin averaged 2.65cm, which is more than 0.8cm smaller than in the previous studies. Furthermore, it takes an average of 7.9ms to solve one frame, enabling online real-time self-collision resolution.