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.
Abstract (translated)
从裁剪图案自动组装服装仍然是一个重大挑战，因为缺乏标准化的标注协议以及语义线索的频繁缺失。现有方法通常依赖于面板标签或手工制作的经验法则，这限制了它们在处理现实世界中不规则图案时的应用能力。我们提出了AutoSew，这是一个完全自动化的基于几何的方法，可以从2D图案轮廓直接预测缝合对应关系。AutoSew将问题表述为图匹配任务，利用图神经网络捕捉局部和全局的几何上下文，并采用可微分最优传输求解器来推断包括多边连接在内的缝合关系。 为了支持这一任务，我们更新了GarmentCodeData数据集，修改超过18,000个图案并添加了现实主义的多边注释，反映工业组装场景。AutoSew达到了96%的F1分数，并成功地将测试服装中的73.3%无误地组合在一起，在仅依赖几何输入的情况下超越了现有方法。 我们的结果表明，仅凭几何信息就可以稳健地指导缝合预测，使没有手动干预的大规模服装组装成为可能。
URL
https://arxiv.org/abs/2602.22052
