Neural Garment Dynamics via Manifold-Aware Transformers

Abstract

Data driven and learning based solutions for modeling dynamic garments have significantly advanced, especially in the context of digital humans. We model the dynamics of a garment by exploiting its local interactions with the underlying human body. At the core of our approach is a mesh-agnostic garment representation and a manifold-aware transformer network design, which together enable our method to generalize to unseen garment and body geometries.