从2D裁片自动生成缝合关系，让数字服装建模更智能。
Learning-based Seam Correspondence Reconstruction in Sewing Patterns
- 用图神经网络推断裁片的粗粒度连接与细粒度缝线对应关系。
- 在多种复杂缝线结构下仍保持高准确率，跨款式泛化能力强。
- 适合服装数字化、3D虚拟试衣等场景，减少人工标注成本。
数字服装裁片通常由分离的2D面板构成，且无明确缝线标注，导致后续3D建模依赖繁琐的人工设定。本文提出一种基于图的学习框架，仅通过2D裁片几何信息重建两级缝合信息：粗粒度的面板连接关系和细粒度的缝线对应关系。在粗粒度层面，通过预测与人体解剖区域相关的面板语义，确保与人体结构及服装设计规范一致；在细粒度层面，基于重构的面板图，利用图消息传递学习隐式边表示，联合编码局部缝线几何与全局服装上下文，进而解码生成详细缝线对应。该方法支持复杂拓扑结构，包括多对一对应、面板内缝线及弯曲缝线。实验表明其缝合精度高，且在不同服装风格间具有强泛化能力。
原文摘要 · Abstract (English)
Digital sewing patterns typically consist of disjoint 2D panels without explicit stitch annotations, making downstream 3D modeling reliant on labor-intensive expert specification. In this paper, we present a graph-based learning framework that reconstructs two-level stitching information, coarse panel connectivity and fine-grained seam correspondence, from 2D panel geometry alone. At the coarse level, panel connectivity is inferred by predicting panel semantics associated with anatomical body regions, enforcing consistency with body structure and garment design conventions. Based on the reconstructed panel graph, fine-grained seam correspondences between panel pairs are inferred by learning latent edge representations that jointly encode local seam geometry and global garment context through graph message passing. The resulting edge embeddings are subsequently decoded into detailed seam correspondences. Our method supports complex sewing-pattern topologies, including many-to-one correspondences, intra-panel seams, and curved seams. Experiments demonstrate high stitching accuracy and strong generalization across garment styles.
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