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arXiv · 2610.14200

ClothCode: Generative Modeling for Garment State Estimation and Dynamics Prediction

Yingdong Ru·Lipeng Zhuang·Shiyu Fan·Zhuo He·Florent P. Audonnet·Gerardo Aragon Camarasa
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Abstract

We introduce ClothCode, a unified generative framework for garment state estimation and action-conditioned dynamics prediction in a shared discrete configuration space. A material-aligned tokenizer encodes garment configuration as residual vector-quantized patch tokens, while task-specific Masked Generative Transformers predict these tokens through parallel confidence-guided refinement. Across three garment templates, ClothCode reduces mean displacement error by 26.6% for state estimation and 37.0% for dynamics prediction relative to the strongest baselines. Dynamics prediction requires only 16.7 ms per window, compared with 556.2 ms for diffusion. Integrated into the same model-predictive controller, ClothCode improves real-world folding and flattening success from 56.7% to 78.9%. Further results and videos are available at: https://anonymous.4open.science/r/ClothCode-DE32

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Yingdong Ru, Lipeng Zhuang, Shiyu Fan, Zhuo He, Florent P. Audonnet, Gerardo Aragon Camarasa. 2026-10-09. ClothCode: Generative Modeling for Garment State Estimation and Dynamics Prediction. https://arxiv.org/abs/2610.14200

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