  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.
