Pb4U-GNet: Resolution-Adaptive Garment Simulation via Propagation-before-Update Graph Network

• URL: http://arxiv.org/abs/2601.15110v1
• Date: Wed, 21 Jan 2026 15:50:30 GMT
• Title: Pb4U-GNet: Resolution-Adaptive Garment Simulation via Propagation-before-Update Graph Network
• Authors: Aoran Liu, Kun Hu, Clinton Ansun Mo, Qiuxia Wu, Wenxiong Kang, Zhiyong Wang,
• Abstract summary: Garment simulation is fundamental to various applications in computer vision and graphics, from virtual try-on to digital human modelling.<n>Existing approaches exhibit poor cross-resolution generalisation, demonstrating significant performance degradation on higher-resolution meshes.<n>We introduce Pb4U-GNet, a resolution-adaptive framework that decouples message propagation from feature updates.
• Score: 47.339059283454844
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Garment simulation is fundamental to various applications in computer vision and graphics, from virtual try-on to digital human modelling. However, conventional physics-based methods remain computationally expensive, hindering their application in time-sensitive scenarios. While graph neural networks (GNNs) offer promising acceleration, existing approaches exhibit poor cross-resolution generalisation, demonstrating significant performance degradation on higher-resolution meshes beyond the training distribution. This stems from two key factors: (1) existing GNNs employ fixed message-passing depth that fails to adapt information aggregation to mesh density variation, and (2) vertex-wise displacement magnitudes are inherently resolution-dependent in garment simulation. To address these issues, we introduce Propagation-before-Update Graph Network (Pb4U-GNet), a resolution-adaptive framework that decouples message propagation from feature updates. Pb4U-GNet incorporates two key mechanisms: (1) dynamic propagation depth control, adjusting message-passing iterations based on mesh resolution, and (2) geometry-aware update scaling, which scales predictions according to local mesh characteristics. Extensive experiments show that even trained solely on low-resolution meshes, Pb4U-GNet exhibits strong generalisability across diverse mesh resolutions, addressing a fundamental challenge in neural garment simulation.

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