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arXiv · 2504.20409

GarmentX: Autoregressive Parametric Representations for High-Fidelity 3D Garment Generation

Jingfeng Guo·Jinnan Chen·Weikai Chen·Zhenyu Sun·Lanjiong Li·Baozhu Zhao·Lingting Zhu·Xin Wang·Qi Liu
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Abstract

This work presents GarmentX, a novel framework for generating diverse, high-fidelity, and wearable 3D garments from a single input image. Traditional garment reconstruction methods directly predict 2D pattern edges and their connectivity, an overly unconstrained approach that often leads to severe self-intersections and physically implausible garment structures. In contrast, GarmentX introduces a structured and editable parametric representation compatible with GarmentCode, ensuring that the decoded sewing patterns always form valid, simulation-ready 3D garments while allowing for intuitive modifications of garment shape and style. To achieve this, we employ a masked autoregressive model that sequentially predicts garment parameters, leveraging autoregressive modeling for structured generation while mitigating inconsistencies in direct pattern prediction. Additionally, we introduce GarmentX dataset, a large-scale dataset of 378,682 garment parameter-image pairs, constructed through an automatic data generation pipeline that synthesizes diverse and high-quality garment images conditioned on parametric garment representations. Through integrating our method with GarmentX dataset, we achieve state-of-the-art performance in geometric fidelity and input image alignment, significantly outperforming prior approaches. We will release GarmentX dataset upon publication.

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Jingfeng Guo, Jinnan Chen, Weikai Chen, Zhenyu Sun, Lanjiong Li, Baozhu Zhao, Lingting Zhu, Xin Wang, Qi Liu. 2025-04-29. GarmentX: Autoregressive Parametric Representations for High-Fidelity 3D Garment Generation. https://arxiv.org/abs/2504.20409

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