ClothCode: Generative Modeling for Garment State Estimation and Dynamics Prediction

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: this https URL

Robotics (cs.RO); Computer Vision and Pattern Recognition (cs.CV)

arXiv:2610.14200 [cs.RO]

(or arXiv:2610.14200v1 [cs.RO] for this version)

https://doi.org/10.48550/arXiv.2610.14200 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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