  Data driven and learning based solutions for modeling dynamic garments have
significantly advanced, especially in the context of digital humans. However,
existing approaches often focus on modeling garments with respect to a fixed
parametric human body model and are limited to garment geometries that were
seen during training. In this work, we take a different approach and model the
dynamics of a garment by exploiting its local interactions with the underlying
human body. Specifically, as the body moves, we detect local garment-body
collisions, which drive the deformation of the garment. 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. We evaluate our approach on a wide variety
of garment types and motion sequences and provide competitive qualitative and
quantitative results with respect to the state of the art.
