面向自动数字服装装配
Towards automatic digital garment assembly
AI总结：
该研究针对数字服装装配耗时问题，提出基于学习的线段级缝纫模型与纸样片级排列模型，在4035个专业服装数据集上分别实现63.3%、66.2%的测试准确率，可集成至服装CAD软件完成自动装配。
摘要：
从现有二维纸样创建数字服装仍然是一个耗时的过程。尽管纸样以数字形式广泛存在，但在服装CAD软件中对其进行装配需要手动定义缝纫操作和纸样片的排列，以进行悬垂模拟。我们提出两种基于学习的方法，仅以二维矢量纸样作为输入，自动完成这两项任务。我们的自动缝纫模型在线段层面运行，利用带有多个预测头的Transformer编码器。我们的新颖架构支持多对多的有序线段对应关系，以及折边、多层服装所需的重叠接缝，还有成品虚拟服装所需的缝份和针迹类型等构造属性，这些均被先前方法忽略。互补的纸样片层面模型预测身体区域分配、层数和放置坐标，以初始化悬垂模拟。我们在包含4035个专业装配服装实例的精选数据集上评估这两个模型。缝纫模型准确恢复了63.3%测试服装的所有接缝和折边拓扑，而排列模型满足66.2%测试服装所有纸样片的指定放置标准。我们还将这两种方法集成到服装CAD软件中，展示了成功预测服装的自动装配。
英文摘要：
Creating digital garments from existing two-dimensional patterns remains a time-consuming process. Although patterns are widely available in digital form, their assembly in garment CAD software involves manual definition of sewing operations and arrangement of the pattern panels for drape simulation. We present two learning-based approaches that automate both tasks using the 2D vector pattern as their sole input. Our automatic sewing model operates at the segment level, leveraging a Transformer encoder with multiple prediction heads. Our novel architecture supports many-to-many, ordered segment correspondences, as well as hems, overlapping seams needed for multilayer garments, and construction properties such as seam allowances and stitch types, required for the finished virtual garment but neglected by previous methods. A complementary panel-level model predicts body-region assignments, layers, and placement coordinates to initialize the drape simulation. We evaluate both models on a curated dataset of 4,035 professionally assembled garment instances. The sewing model exactly recovers all seam and hem topologies of 63.3\% of test garments, while the arrangement model meets the specified placement criteria for all panels in 66.2\% of test garments. We also integrate both methods into garment CAD software, demonstrating automatic assembly of successfully predicted garments.
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