Frequency-Guided Self-Supervised Wind-Driven Garment Animation Simulation

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dc.contributor.authorShi, Minen_US
dc.contributor.authorLi, Zhenyuen_US
dc.contributor.authorMao, Tianluen_US
dc.contributor.authorZhu, Dengmingen_US
dc.contributor.authorWang, Suqingen_US
dc.contributor.editorChristie, Marcen_US
dc.contributor.editorHan, Ping-Hsuanen_US
dc.contributor.editorLin, Shih-Syunen_US
dc.contributor.editorPietroni, Nicoen_US
dc.contributor.editorSchneider, Teseoen_US
dc.contributor.editorTsai, Hsin-Rueyen_US
dc.contributor.editorWang, Yu-Shuenen_US
dc.contributor.editorZhang, Eugeneen_US
dc.date.accessioned2025-10-07T06:02:34Z
dc.date.available2025-10-07T06:02:34Z
dc.date.issued2025
dc.description.abstractRealistic simulation of garment deformation under coupled multi-physical fields remains a critical challenge in 3d animation, due to diverse properties of fabric and complex interactions with external forces. Existing methods mainly focus on humandriven garment animation and offer dynamic environmental factors such as wind fields. Moreover, supervised learning methods suffer from strong data dependency and limited generalization. We propose FWGNet, a self-supervised training framework based on graph neural networks (GNNs) that models the physical interaction between garments, the human body and wind as an unified physical system. The framework is trained using differentiable physics-based constraints. A core component of FWGNet is a wind feature encoder that utilizes wavelet transforms to project wind velocity sequences into the frequency domain, enabling the network to effectively capture multi-scale turbulence effects on fabric behavior. To eliminate dependence on large datasets, we introduce physics-informed loss functions that incorporate gravitational potential energy, aerodynamic wind forces, and fabric deformation constraints. Experiments demonstrate that our approach produces highly realistic visual effects, such as detailed wrinkle formation and fabric fluttering under dynamic wind conditions. Quantitative evaluations across physical metrics confirm that FWGNet achieves a strong balance between physical accuracy and visual realism, particularly in complex scenarios involving coupled physical interactions.en_US
dc.description.sectionheadersDigital Clothing
dc.description.seriesinformationPacific Graphics Conference Papers, Posters, and Demos
dc.identifier.doi10.2312/pg.20251260
dc.identifier.isbn978-3-03868-295-0
dc.identifier.pages9 pages
dc.identifier.urihttps://doi.org/10.2312/pg.20251260
dc.identifier.urihttps://diglib.eg.org/handle/10.2312/pg20251260
dc.publisherThe Eurographics Associationen_US
dc.rightsAttribution 4.0 International License
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectCCS Concepts: Computing methodologies → Animation
dc.subjectComputing methodologies → Animation
dc.titleFrequency-Guided Self-Supervised Wind-Driven Garment Animation Simulationen_US
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