AutoSew: A Geometric Approach to Stitching Prediction with Graph Neural Networks

2026-02-25

AI Summary

This study addresses the challenge of automatically predicting sewing correspondences from unlabeled 2D garment patterns lacking semantic cues. While existing approaches rely on manual annotations or heuristic rules, this work presents the first fully geometry-driven, end-to-end solution by formulating seam prediction as a graph matching problem. Leveraging only the geometric contours of patterns, the method employs a graph neural network to capture both local and global contextual information and integrates a differentiable optimal transport solver to infer sewing relationships—including complex multi-edge connections. The authors introduce an updated GarmentCode dataset with multi-edge seam annotations that better reflect real-world industrial scenarios. On this benchmark, the proposed approach achieves a 96% F1 score and enables error-free full assembly for 73.3% of test garments, substantially outperforming current state-of-the-art methods.

Technology Category

• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Computer Vision: Segmentation
• Search and Optimization: Learning to Search

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets

Abstract

Automating garment assembly from sewing patterns remains a significant challenge due to the lack of standardized annotation protocols and the frequent absence of semantic cues. Existing methods often rely on panel labels or handcrafted heuristics, which limit their applicability to real-world, non-conforming patterns. We present AutoSew, a fully automatic, geometry-based approach for predicting stitch correspondences directly from 2D pattern contours. AutoSew formulates the problem as a graph matching task, leveraging a Graph Neural Network to capture local and global geometric context, and employing a differentiable optimal transport solver to infer stitching relationships-including multi-edge connections. To support this task, we update the GarmentCodeData dataset modifying over 18k patterns with realistic multi-edge annotations, reflecting industrial assembly scenarios. AutoSew achieves 96% F1-score and successfully assembles 73.3% of test garments without error, outperforming existing methods while relying solely on geometric input. Our results demonstrate that geometry alone can robustly guide stitching prediction, enabling scalable garment assembly without manual input.

Problem

• garment assembly
• stitching prediction
• sewing patterns
• geometric approach
• graph matching

Innovation

• Graph Neural Networks
• Geometric Representation
• Differentiable Optimal Transport
• Stitching Prediction
• Garment Assembly