Self-Supervised Multi-Layer Garment Animation Generation Network

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dc.contributor.authorHan, Guoqingen_US
dc.contributor.authorShi, Minen_US
dc.contributor.authorMao, Tianluen_US
dc.contributor.authorWang, Xinranen_US
dc.contributor.authorZhu, Dengmingen_US
dc.contributor.authorGao, Linen_US
dc.contributor.editorChen, Renjieen_US
dc.contributor.editorRitschel, Tobiasen_US
dc.contributor.editorWhiting, Emilyen_US
dc.date.accessioned2024-10-13T18:04:59Z
dc.date.available2024-10-13T18:04:59Z
dc.date.issued2024
dc.description.abstractThis 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.en_US
dc.description.sectionheadersGarment Modeling and Simulation
dc.description.seriesinformationPacific Graphics Conference Papers and Posters
dc.identifier.doi10.2312/pg.20241307
dc.identifier.isbn978-3-03868-250-9
dc.identifier.pages8 pages
dc.identifier.urihttps://doi.org/10.2312/pg.20241307
dc.identifier.urihttps://diglib.eg.org/handle/10.2312/pg20241307
dc.publisherThe Eurographics Associationen_US
dc.rightsAttribution 4.0 International License
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectKeywords: Multi-layer garments animation;Computer Graphics;Self-supervised Learning;Garment deformation; CCS Concepts: Computing methodologies → Animation
dc.subjectMulti
dc.subjectlayer garments animation
dc.subjectComputer Graphics
dc.subjectSelf
dc.subjectsupervised Learning
dc.subjectGarment deformation
dc.subjectCCS Concepts
dc.subjectComputing methodologies → Animation
dc.titleSelf-Supervised Multi-Layer Garment Animation Generation Networken_US
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