Affiliation:
1. Nanjing University of Science and Technology, China, University College London, United Kingdom
2. Adobe Research, United Kingdom
3. University College London and Adobe Research, United Kingdom
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 roll-out prediction. We demonstrate plausible generalization to unseen body shapes and motion inputs, and show improvements over multiple state-of-the-art alternatives.
Code and data is released in https://geometry.cs.ucl.ac.uk/projects/2022/MotionDeepGarment/
Funder
Marie Sk?odowska-Curie grant agreement
ERC SmartGeometry
Publisher
Association for Computing Machinery (ACM)
Subject
Computer Graphics and Computer-Aided Design
Cited by
3 articles.
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1. MeshGraphNetRP: Improving Generalization of GNN-based Cloth Simulation;ACM SIGGRAPH Conference on Motion, Interaction and Games;2023-11-15
2. HOOD: Hierarchical Graphs for Generalized Modelling of Clothing Dynamics;2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR);2023-06
3. GenSim: Unsupervised Generic Garment Simulator;2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW);2023-06