Learning-based Seam Correspondence Reconstruction in Sewing Patterns

2026-07-23

AI Summary

This work addresses the challenge that digital sewing patterns typically lack explicit stitching annotations, necessitating expert intervention to define seam relationships for 3D garment modeling. To overcome this limitation, the authors propose a graph learning–based framework that automatically reconstructs two-level stitching information—coarse-grained inter-pattern connectivity and fine-grained seam correspondence—solely from the 2D pattern geometry. The method uniquely integrates pattern semantics, human body structural constraints, and garment design conventions within a unified model, enabling robust handling of complex topologies such as many-to-one, intra-pattern, and curved seams. By leveraging graph neural networks to fuse local geometric features with global contextual cues, the framework generates high-fidelity edge embeddings and decodes precise seam correspondences. Experiments demonstrate that the approach achieves high reconstruction accuracy across diverse garment styles, exhibits strong generalization, and effectively manages intricate sewing configurations.

Abstract

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.

Problem

• seam correspondence
• sewing patterns
• 3D garment modeling
• panel connectivity
• stitch annotation

Innovation

• graph-based learning
• seam correspondence
• panel connectivity
• garment modeling
• message passing