[CVPR 2026] PhysSkin: Real-Time and Generalizable Physics-Based Animation via Self-Supervised Neural Skinning

Project Page | Arxiv | Supplementary

PhysSkin: Real-Time and Generalizable Physics-Based Animation via Self-Supervised Neural Skinning,
Yuanhang Lei, Tao Cheng, Xingxuan Li, Boming Zhao, Siyuan Huang, Ruizhen Hu, Peter Yichen Chen, Hujun Bao, Zhaopeng Cui†

Abstract: Achieving real-time physics-based animation that generalizes across diverse 3D shapes and discretizations remains a fundamental challenge. We introduce PhysSkin, a physics-informed framework that addresses this challenge. In the spirit of Linear Blend Skinning, we learn continuous skinning fields as basis functions lifting motion subspace coordinates to full-space deformation, with subspace defined by handle transformations. To generate mesh-free, discretization-agnostic, and physically consistent skinning fields that generalize well across diverse 3D shapes, PhysSkin employs a new neural skinning fields autoencoder which consists of a transformer-based encoder and a cross-attention decoder. Furthermore, we also develop a novel physics-informed self-supervised learning strategy that incorporates on-the-fly skinning-field normalization and conflict-aware gradient correction, enabling effective balancing of energy minimization, spatial smoothness, and orthogonality constraints. PhysSkin shows outstanding performance on generalizable neural skinning and enables real-time physics-based animation.

Method Overview

ToDos

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• Release project page.
• Release paper.
• Release codes.

Acknowledgement

This codebase used lots of source code from:

1. Simplicts
2. Michelangelo
3. ConFIG

We thank the authors of these projects.