Self-Supervised Humidity-Controllable Garment Simulation via Capillary Bridge Modeling
| dc.contributor.author | Shi, Min | en_US |
| dc.contributor.author | Wang, Xinran | en_US |
| dc.contributor.author | Zhang, Jia-Qi | en_US |
| dc.contributor.author | Gao, Lin | en_US |
| dc.contributor.author | Zhu, Dengming | en_US |
| dc.contributor.author | Zhang, Hongyan | en_US |
| dc.contributor.editor | Christie, Marc | en_US |
| dc.contributor.editor | Pietroni, Nico | en_US |
| dc.contributor.editor | Wang, Yu-Shuen | en_US |
| dc.date.accessioned | 2025-10-07T05:01:46Z | |
| dc.date.available | 2025-10-07T05:01:46Z | |
| dc.date.issued | 2025 | |
| dc.description.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. | en_US |
| dc.description.number | 7 | |
| dc.description.sectionheaders | Digital Clothing | |
| dc.description.seriesinformation | Computer Graphics Forum | |
| dc.description.volume | 44 | |
| dc.identifier.doi | 10.1111/cgf.70236 | |
| dc.identifier.issn | 1467-8659 | |
| dc.identifier.pages | 10 pages | |
| dc.identifier.uri | https://doi.org/10.1111/cgf.70236 | |
| dc.identifier.uri | https://diglib.eg.org/handle/10.1111/cgf70236 | |
| dc.publisher | The Eurographics Association and John Wiley & Sons Ltd. | en_US |
| dc.subject | CCS Concepts: Computing methodologies → Animation | |
| dc.subject | Computing methodologies → Animation | |
| dc.title | Self-Supervised Humidity-Controllable Garment Simulation via Capillary Bridge Modeling | en_US |