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Garments are ubiquitous in both real and many of the virtual worlds. They arehighly deformable objects, exhibit an immense variety of designs and shapes,and yet, most garments are created from a set of regularly shaped flat pieces.Exploration of garment structure presents a peculiar case for an objectstructure estimation task and might prove useful for downstream tasks of neural3D garment modeling and reconstruction by providing strong prior on garmentshapes. To facilitate research in these directions, we propose a method forgenerating large synthetic datasets of 3D garment designs and their sewingpatterns. Our method consists of a flexible description structure forspecifying parametric sewing pattern templates and the automatic generationpipeline to produce garment 3D models with little-to-none manual intervention.To add realism, the pipeline additionally creates corrupted versions of thefinal meshes that imitate artifacts of 3D scanning.  With this pipeline, we created the first large-scale synthetic dataset of 3Dgarment models with their sewing patterns. The dataset contains more than 20000garment design variations produced from 19 different base types. Seven of thesegarment types are specifically designed to target evaluation of thegeneralization across garment sewing pattern topologies.

Generating Datasets of 3D Garments with Sewing Patterns