CPSN: Collision‐Overlap and Physics‐Based Self‐Supervised Neural Cloth Simulation

Self‐collision handling remains a fundamental and long‐standing challenge in neural cloth simulation, particularly for loose garments with complex topologies. We propose a self‐supervised neural cloth simulation framework that integrates Gaussian mixture skinning (GMS) with a differentiable collision‐overlap loss to significantly enhance physical plausibility and visual realism. We employ continuous and spatially smooth GMS weights to model vertex‐skeleton coupling, enabling stable deformations under large body motions. To explicitly address cloth self‐collisions, we introduce a differentiable spatial repulsion constraint that suppresses interpenetration and layer‐overlap artifacts. The proposed objective is jointly optimized with physics‐inspired losses, enabling the network to learn consistent cloth dynamics without relying on ground‐truth physical simulations. Experimental results demonstrate improved temporal stability, reduced collision artifacts, and stronger generalization compared to existing self‐supervised methods.