Self‐Supervised Humidity‐Controllable Garment Simulation via Capillary Bridge Modeling

Abstract

Abstract 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.