ClothCombo: Modeling Inter-Cloth Interaction for Draping Multi-Layered Clothes
Authors: 
Dohae Lee, Hyun Kang, In-Kwon LeeAuthors Info & Claims
Dohae Lee, Hyun Kang, In-Kwon LeeAuthors Info & ClaimsArticle No.: 247, Pages 1 - 13
Abstract
We present ClothCombo, a pipeline to drape arbitrary combinations of clothes on 3D human models with varying body shapes and poses. While existing learning-based approaches for draping clothes have shown promising results, multi-layered clothing remains challenging as it is non-trivial to model inter-cloth interaction. To this end, our method utilizes a GNN-based network to efficiently model the interaction between clothes in different layers, thus enabling multi-layered clothing. Specifically, we first create feature embedding for each cloth using a topology-agnostic network. Then, the draping network deforms all clothes to fit the target body shape and pose without considering inter-cloth interaction. Lastly, the untangling network predicts the per-vertex displacements in a way that resolves interpenetration between clothes. In experiments, the proposed model demonstrates strong performance in complex multi-layered scenarios. Being agnostic to cloth topology, our method can be readily used for layered virtual try-on of real clothes in diverse poses and combinations of clothes.
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- ClothCombo: Modeling Inter-Cloth Interaction for Draping Multi-Layered Clothes
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Published: 05 December 2023
Published in TOG Volume 42, Issue 6
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