无需物理模拟，统一3D服装与2D图案的生成空间。
Stitched Embeddings: A Unified Latent Space for 3D Garments and 2D Patterns
- 用BoxMesh作为中间表示，实现2D裁片到3D服装的直接映射。
- 在无仿真条件下达到当前最佳的图案重建精度。
- 支持从3D网格恢复2D图案，适合虚拟试衣与设计工具开发。
尽管服装对数字人的真实感至关重要，但其拓扑多样性使其建模难度远超参数化人体。传统制衣依赖2D剪裁图样，但将图样映射为3D几何通常需物理仿真。本文提出Stitched Embeddings，首个无需仿真、在单一双向潜在空间中统一3D服装重建与2D图案推断的框架。通过利用预训练3D基础模型的几何先验，克服了高质量服装建模的数据稀缺问题。我们提出以BoxMesh作为关键中间表示，将2D面板对齐至3D结构，且无需模拟器带来的计算开销。该架构在图案重建上达到业界最优性能，同时显著提升效率。此外，可微分流程支持新应用：从网格恢复2D图案，以及从2D图样编辑3D服装。本工作为神经3D视觉与实际服装制造流程提供了可扩展的连接。
原文摘要 · Abstract (English)
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
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