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.

翻译：摘要：实现跨多种三维形状与离散化形式均具有泛化能力的实时物理动画，仍是基础性挑战。本文提出物理感知框架PhysSkin以应对该问题。受线性混合蒙皮思想启发，我们学习连续蒙皮场作为基函数，将运动子空间坐标提升至全空间变形，其中子空间由控制柄变换定义。为生成无网格、离散化无关且物理一致的蒙皮场，使其能良好泛化至不同三维形状，PhysSkin采用新型神经蒙皮场自编码器，该结构包含基于Transformer的编码器与交叉注意力解码器。此外，我们提出一种新颖的物理感知自监督学习策略，集成动态蒙皮场归一化与冲突感知梯度校正，有效平衡能量最小化、空间平滑性与正交性约束。PhysSkin在可泛化神经蒙皮任务中展现出卓越性能，并实现了实时物理动画。