Recent research interest in the learning-based processing of garments, from virtual fitting to generation and reconstruction, stumbles on a scarcity of high-quality public data in the domain. We contribute to resolving this need by presenting the first large-scale synthetic dataset of 3D made-to-measure garments with sewing patterns, as well as its generation pipeline. GarmentCodeData contains 115,000 data points that cover a variety of designs in many common garment categories: tops, shirts, dresses, jumpsuits, skirts, pants, etc., fitted to a variety of body shapes sampled from a custom statistical body model based on CAESAR, as well as a standard reference body shape, applying three different textile materials. To enable the creation of datasets of such complexity, we introduce a set of algorithms for automatically taking tailor's measures on sampled body shapes, sampling strategies for sewing pattern design, and propose an automatic, open-source 3D garment draping pipeline based on a fast XPBD simulator, while contributing several solutions for collision resolution and drape correctness to enable scalability. Dataset: http://hdl.handle.net/20.500.11850/673889

通过构建生成管道和算法，我们提供了首个大规模的合成数据集，其中包含115,000个关键数据点，涵盖了多种常见服装类别的设计和多样化的身体形状，并应用了三种不同的纺织材料，从而解决了学习处理服装方面存在的数据缺乏问题，实现了可扩展性的自动化3D服装披肩。

GarmentCodeData：一种带有缝纫图案的3D定制服装数据集