44-Issue 7

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https://diglib.eg.org/handle/10.2312/3607235

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Browsing 44-Issue 7 by Subject "CCS Concepts: Computing methodologies → Animation"

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    Self-Supervised Humidity-Controllable Garment Simulation via Capillary Bridge Modeling
    (The Eurographics Association and John Wiley & Sons Ltd., 2025) Shi, Min; Wang, Xinran; Zhang, Jia-Qi; Gao, Lin; Zhu, Dengming; Zhang, Hongyan; Christie, Marc; Pietroni, Nico; Wang, Yu-Shuen
    Simulating wet clothing remains a significant challenge due to the complex physical interactions between moist fabric and the human body, compounded by the lack of dedicated datasets for training data-driven models. Existing self-supervised approaches struggle to capture moisture-induced dynamics such as skin adhesion, anisotropic surface resistance, and non-linear wrinkling, leading to limited accuracy and efficiency. To address this, we present SHGS, a novel self-supervised framework for humidity-controllable clothing simulation grounded in the physical modeling of capillary bridges that form between fabric and skin. We abstract the forces induced by wetness into two physically motivated components: a normal adhesive force derived from Laplace pressure and a tangential shear-resistance force that opposes relative motion along the fabric surface. By formulating these forces as potential energy for conservative effects and as mechanical work for non-conservative effects, we construct a physics-consistent wetness loss. This enables self-supervised training without requiring labeled data of wet clothing. Our humidity-sensitive dynamics are driven by a multi-layer graph neural network, which facilitates a smooth and physically realistic transition between different moisture levels. This architecture decouples the garment's dynamics in wet and dry states through a local weight interpolation mechanism, adjusting the fabric's behavior in response to varying humidity conditions. Experiments demonstrate that SHGS outperforms existing methods in both visual fidelity and computational efficiency, marking a significant advancement in realistic wet-cloth simulation.