  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
