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

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pg20251308.pdf (3.44 MB)
Date
2025
Authors
Shi, Min
Li, Zhenyu
Mao, Tianlu
Zhu, Dengming
Wang, Suqing
Journal Title
Journal ISSN
Volume Title
Publisher
The Eurographics Association
Abstract
Realistic simulation of garment deformation under coupled multi-physical fields remains a critical challenge in 3D animation, primarily due to diverse properties of fabric and complex interactions with external forces. Existing methods predominantly focus on human-driven garment animation and struggle with complex environmental factors such as wind fields, while supervised learning methods suffer from strong data dependency and limited generalization. We propose FWGNet, a self-supervised training framework based on GNNs that models the garment-human-wind interaction as an integrated physical system. The framework is trained using differentiable physics-based constraints. A key component is a wind feature encoder that employs wavelet transforms to map wind velocity sequences into the frequency domain, effectively capturing 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 multiple metrics confirm that FWGNet achieves a strong balance between physical accuracy and visual realism, particularly in complex scenarios involving coupled physical interactions.
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CCS Concepts: Computing methodologies → Animation

        
@inproceedings{
10.2312:pg.20251308
,
booktitle = {
Pacific Graphics Conference Papers, Posters, and Demos
},
editor = {
Christie, Marc
 and 
Han, Ping-Hsuan
 and 
Lin, Shih-Syun
 and 
Pietroni, Nico
 and 
Schneider, Teseo
 and 
Tsai, Hsin-Ruey
 and 
Wang, Yu-Shuen
 and 
Zhang, Eugene
},
title = {{
Frequency-Guided Self-Supervised Wind-Driven Garment Animation Simulation
}},
author = {
Shi, Min
 and 
Li, Zhenyu
 and 
Mao, Tianlu
 and 
Zhu, Dengming
 and 
Wang, Suqing
},
year = {
2025
},
publisher = {
The Eurographics Association
},
ISBN = {
978-3-03868-295-0
},
DOI = {
10.2312/pg.20251308
}
}
Citation
URI
https://doi.org/10.2312/pg.20251308
 https://diglib.eg.org/handle/10.2312/pg20251308
Collections
PG2025 Conference Papers, Posters, and Demos