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arXiv 2607.00829cs.CV
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缝合嵌入：3D服装与2D纸样的统一潜在空间

Stitched Embeddings: A Unified Latent Space for 3D Garments and 2D Patterns

University of Tübingen（图宾根大学）；Tübingen AI Center（图宾根人工智能中心）；Technical University of Munich（慕尼黑工业大学）；Munich Center for Machine Learning（慕尼黑机器学习中心）；Max Planck Institute for Informatics（马克斯·普朗克信息学研究所）；Meta

Andrea Sanchietti, Riccardo Marin, Bharat Lal Bhatnagar, Yuanlu Xu, Gerard Pons-Moll

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AI总结：

提出首个无需模拟的框架Stitched Embeddings，通过BoxMesh中间表示在统一双向潜在空间中实现3D服装重建与2D纸样推断，达到高精度高效率。

摘要：

虽然服装对于逼真的数字人类至关重要，但其拓扑多样性使得建模比参数化身体困难得多。传统裁剪依赖于2D缝纫纸样，但目前将这些纸样与3D几何连接需要物理模拟。我们提出了Stitched Embeddings，这是第一个无需模拟的框架，在单个双向潜在空间内统一了3D服装重建和缝纫纸样推断。通过利用预训练3D基础模型的几何先验，我们的方法克服了高质量服装建模通常面临的数据稀缺问题。我们提出使用BoxMesh作为关键中间表示，将2D面板对齐到3D配置，而无需模拟器的计算开销。该架构在纸样重建中达到了最先进的精度，同时显著提高了效率。此外，我们的可微管道支持新颖的应用，包括从网格恢复纸样和从2D纸样进行3D编辑。最后，这项工作为神经3D视觉与物理服装制造流程之间提供了可扩展的桥梁。项目页面：this https URL

英文摘要：

While garments are essential for realistic digital humans, their topological variety makes them much harder to model than parametric bodies. Traditional tailoring relies on 2D sewing patterns, yet bridging these patterns to 3D geometry currently requires physical simulations. We present Stitched Embeddings, the first simulation-free framework to unify 3D garment reconstruction and sewing pattern inference within a single bidirectional latent space. By leveraging the geometric priors of a pretrained 3D foundation model, our approach overcomes the data scarcity typically associated with high-quality garment modeling. We propose to use the BoxMesh as a critical intermediate representation to align 2D panels into 3D configurations without the computational overhead of a simulator. This architecture achieves state-of-the-art accuracy in pattern reconstruction while significantly improving efficiency. Furthermore, our differentiable pipeline enables novel applications, including pattern recovery from meshes and 3D editing from 2D patterns. Finally, this work provides a scalable link between neural 3D vision and the physical garment manufacturing pipeline. Project Page: https://andreus00.github.io/stitchedembeddings

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。

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