  Garments are ubiquitous in both real and many of the virtual worlds. They are
highly 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 object
structure estimation task and might prove useful for downstream tasks of neural
3D garment modeling and reconstruction by providing strong prior on garment
shapes. To facilitate research in these directions, we propose a method for
generating large synthetic datasets of 3D garment designs and their sewing
patterns. Our method consists of a flexible description structure for
specifying parametric sewing pattern templates and the automatic generation
pipeline to produce garment 3D models with little-to-none manual intervention.
To add realism, the pipeline additionally creates corrupted versions of the
final meshes that imitate artifacts of 3D scanning.
  With this pipeline, we created the first large-scale synthetic dataset of 3D
garment models with their sewing patterns. The dataset contains more than 20000
garment design variations produced from 19 different base types. Seven of these
garment types are specifically designed to target evaluation of the
generalization across garment sewing pattern topologies.
