  Realistic dynamic garments on animated characters have many AR/VR
applications. While authoring such dynamic garment geometry is still a
challenging task, data-driven simulation provides an attractive alternative,
especially if it can be controlled simply using the motion of the underlying
character. In this work, we focus on motion guided dynamic 3D garments,
especially for loose garments. In a data-driven setup, we first learn a
generative space of plausible garment geometries. Then, we learn a mapping to
this space to capture the motion dependent dynamic deformations, conditioned on
the previous state of the garment as well as its relative position with respect
to the underlying body. Technically, we model garment dynamics, driven using
the input character motion, by predicting per-frame local displacements in a
canonical state of the garment that is enriched with frame-dependent skinning
weights to bring the garment to the global space. We resolve any remaining
per-frame collisions by predicting residual local displacements. The resultant
garment geometry is used as history to enable iterative rollout prediction. We
demonstrate plausible generalization to unseen body shapes and motion inputs,
and show improvements over multiple state-of-the-art alternatives.
