Self-Supervised Multi-Layer Garment Animation Generation Network
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Date
2024
Journal Title
Journal ISSN
Volume Title
Publisher
The Eurographics Association
Abstract
This paper presents a self-supervised multi-layer garment animation generation network. The complexity inherent in multi-layer garments, particularly the diverse interactions between layers, poses challenges in generating continuous, stable, physically accurate, and visually realistic garment deformation animations. To tackle these challenges, we present the Self-Supervised Multi-Layer Garment Animation Generation Network (SMLN). The architecture of SMLN is based on graph neural networks, which represents garment models uniformly as graph structures, thereby naturally depicting the hierarchical structure of garments and capturing the relationships between garment layers. Unlike existing multi-layer garment deformation methods, we model interaction forces such as friction and repulsion between garment layers, translating physical laws consistent with dynamics into network constraints. We penalize garment deformation regions that exceed these constraints. Furthermore, instead of the traditional post-processing method of fixed vertex displacement calculation for handling collision interactions, we add an additional repulsion constraint layer within the network to update the corresponding repulsive force acceleration, thereby adaptively managing collisions between garment layers. Our self-supervised modeling approach enables the network to learn without relying on garment sample datasets. Experimental results demonstrate that our method is capable of generating visually plausible multi-layer garment deformation effects, surpassing existing methods in both visual quality and evaluation metrics.
Description
@inproceedings{10.2312:pg.20241307,
booktitle = {Pacific Graphics Conference Papers and Posters},
editor = {Chen, Renjie and Ritschel, Tobias and Whiting, Emily},
title = {{Self-Supervised Multi-Layer Garment Animation Generation Network}},
author = {Han, Guoqing and Shi, Min and Mao, Tianlu and Wang, Xinran and Zhu, Dengming and Gao, Lin},
year = {2024},
publisher = {The Eurographics Association},
ISBN = {978-3-03868-250-9},
DOI = {10.2312/pg.20241307}
}