Pb4U-GNet: Resolution-adaptive garment simulation via propagation-before-update graph network

Author Identifier (ORCID)

Kun Hu: https://orcid.org/0000-0002-6891-8059

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.

Keywords

Pb4U-GNet, garment simulation, graph neural networks, mesh resolution, computer vision, virtual try-on

Document Type

Conference Proceeding

Date of Publication

1-1-2026

Volume

40

Issue

9

Publication Title

Proceedings of the AAAI Conference on Artificial Intelligence

Publisher

Association for the Advancement of Artificial Intelligence

School

School of Science

Funders

This work was supported by the Australian Research Council (ARC) Linkage Project #LP230100294 and ECU Science Early Career and New Staff Grant Scheme.

Grant Number

ARC Number : LP230100294

Comments

Liu, A., Hu, K., Mo, C. A., Wu, Q., Kang, W., & Wang, Z. (2026). Pb4U-GNet: Resolution-adaptive garment simulation via propagation-before-update graph network. Proceedings of the AAAI Conference on Artificial Intelligence, 40(9), 7060–7068. https://doi.org/10.1609/aaai.v40i9.37641

Copyright

free_to_read

First Page

7060

Last Page

7068

Link to Full Text

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Link to publisher version (DOI)

10.1609/aaai.v40i9.37641