PhysSkin++: Real-Time and Generalizable Physics-Based Animation via Self-Supervised Neural Skinning

Key points

• Generalizing real-time physics-based animation across diverse 3D shapes and spatial discretizations remains challenging.1
• Specifically, we propose a Neural Skinning Fields Autoencoder that predicts shape-dependent deformation subspaces without per-object network training.1
• To learn these subspaces without simulation trajectories, we develop a Physics-Informed Self-Supervised Learning strategy that combines skinning-field normalization with conflict-aware gradient correction, stabilizing joint optimization under physical and orthogonality constraints.1
• For prescribed rig animation, we further introduce a self-supervised complementarity objective to learn complementary elastic responses without enforcing complementarity constraints at runtime.1

Sources (1)

• [1]PhysSkin++: Real-Time and Generalizable Physics-Based Animation via Self-Supervised Neural Skinning
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 19, 09:25 AM

  In this paper, we introduce PhysSkin++, a physics-informed framework that learns continuous neural skinning fields from static geometries to construct compact deformation subspaces.

  Generalizing real-time physics-based animation across diverse 3D shapes and spatial discretizations remains challenging.

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