Generating Datasets of 3D Garments with Sewing Patterns
This work addresses the need for datasets to facilitate research in neural 3D garment modeling and reconstruction, providing a strong prior on garment shapes, but it is incremental as it builds on existing synthetic data generation techniques.
The authors tackled the problem of generating large synthetic datasets of 3D garments with sewing patterns by proposing a method that uses parametric templates and an automatic pipeline, resulting in a dataset of over 20,000 garment design variations from 19 base types.
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.