基于学习的缝纫图案接缝对应重建

中文摘要 AI 辅助

数字缝纫图案通常由不相交的二维面板组成，没有明确的缝线注释，这使得下游的三维建模依赖于劳动密集型的专家指定。在本文中，我们提出了一个基于图的学习框架，该框架仅从二维面板几何形状重建两级缝线信息，即粗略的面板连接性和细粒度的接缝对应。在粗略级别，通过预测与身体解剖区域相关的面板语义来推断面板连接性，确保与身体结构和服装设计惯例一致。基于重建的面板图，通过学习潜在边缘表示来推断面板对之间的细粒度接缝对应，该表示通过图消息传递联合编码局部接缝几何形状和全局服装上下文。随后将得到的边缘嵌入解码为详细的接缝对应。我们的方法支持复杂的缝纫图案拓扑结构，包括多对一对应、面板内接缝和曲线接缝。实验证明了高缝线精度和跨服装款式的强泛化能力。

英文摘要

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.

发表机构

• Zhejiang Sci-Tech University（浙江理工大学）
• University of Science and Technology of China（中国科学技术大学）
• Style3D Research（模幻科技）

机构由 AI 辅助整理，请以论文原文为准。