Motion Guided Deep Dynamic 3D Garments

TL;DR

This paper introduces a data-driven method for generating realistic, motion-guided dynamic 3D garments that generalize well to unseen shapes and motions, improving over existing approaches.

Contribution

It presents a novel framework that learns a generative space of garments and models motion-dependent deformations conditioned on previous states and body motion.

Findings

Achieves realistic dynamic garment simulations with plausible deformations.

Demonstrates superior generalization to unseen body shapes and motions.

Outperforms multiple state-of-the-art methods in quality and robustness.

Abstract

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.