Frequency-Guided Self-Supervised Wind-Driven Garment Animation Simulation
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Date
2025
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, 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.
Description
CCS Concepts: Computing methodologies → Animation
@inproceedings{10.2312:pg.20251260,
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.20251260}
}