Despite recent success, deep learning-based methods for predicting 3D garment
deformation under body motion suffer from interpenetration problems between the
garment and the body. To address this problem, we propose a novel collision
handling neural network layer called Repulsive Force Unit (ReFU). Based on the
signed distance function (SDF) of the underlying body and the current garment
vertex positions, ReFU predicts the per-vertex offsets that push any
interpenetrating vertex to a collision-free configuration while preserving the
fine geometric details. We show that ReFU is differentiable with trainable
parameters and can be integrated into different network backbones that predict
3D garment deformations. Our experiments show that ReFU significantly reduces
the number of collisions between the body and the garment and better preserves
geometric details compared to prior methods based on collision loss or
post-processing optimization.

提出了一种新的神经网络层 —— 斥力力单位（ReFU）来解决在预测身体运动下的 3D 服装变形时服装和身体之间出现的穿插问题。该方法通过每个顶点的偏移值来解决穿插问题，实验结果显示，相比之前仅基于碰撞损失或后处理优化的方法，ReFU 能够更好地保存与减少物体间碰撞与几何细节。

神经网络中用于服装碰撞处理的排斥力单位

A Repulsive Force Unit for Garment Collision Handling in Neural Networks

Differentiable physics is a powerful approach to learning and control
problems that involve physical objects and environments. While notable progress
has been made, the capabilities of differentiable physics solvers remain
limited. We develop a scalable framework for differentiable physics that can
support a large number of objects and their interactions. To accommodate
objects with arbitrary geometry and topology, we adopt meshes as our
representation and leverage the sparsity of contacts for scalable
differentiable collision handling. Collisions are resolved in localized regions
to minimize the number of optimization variables even when the number of
simulated objects is high. We further accelerate implicit differentiation of
optimization with nonlinear constraints. Experiments demonstrate that the
presented framework requires up to two orders of magnitude less memory and
computation in comparison to recent particle-based methods. We further validate
the approach on inverse problems and control scenarios, where it outperforms
derivative-free and model-free baselines by at least an order of magnitude.

本文介绍了一种可扩展的基于 mesh 的 Differentiable physics 求解器，能够支持大量物体及其交互，并通过稀疏的碰撞处理加速碰撞检测求解。通过实验证明，该框架相比最新的粒子法方法，需要的内存和计算资源少两个数量级，并在控制应用场景中表现优异。