3D Animation Reconstruction Technology for Sports Skills that Integrates Motion Capture and Physical Simulation

Abstract

Motion capture data lack physical constraints such as ground reaction forces and joint torques, leading to penetration and imbalance in the resulting three-dimensional animations. This paper proposes a constraint-adversarial physics-aware reconstruction method for sports skill animation. Physical priors are embedded into motion features through graph convolutional encoding with a differential dynamics loss function. A policy network is then trained via Proximal Policy Optimization, whose reward function combines a Dynamic Time Warping-based style term with angular momentum conservation and contact point stability terms to generate joint driving torques. The poses are updated through implicit integration, and penetration errors are corrected by a Signed Distance Field closed-loop module. Experimental results demonstrate that the method achieves a mean joint position error of 2.3 cm, a physics validity score of 0.92, an average reconstruction time of 18 ms per frame, and a naturalness score of 4.7. The approach preserves motion style while producing physically plausible animation.