GarmentCodeData: A Dataset of 3D Made-to-Measure Garments With Sewing Patterns
<|reference_start|>GarmentCodeData: A Dataset of 3D Made-to-Measure Garments With Sewing Patterns: 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.   Project Page: https://igl.ethz.ch/projects/GarmentCodeData/<|reference_end|>
@article{korosteleva2024garmentcodedata:,
    title={GarmentCodeData: A Dataset of 3D Made-to-Measure Garments With Sewing
  Patterns},
    author={Maria Korosteleva, Timur Levent Kesdogan, Fabian Kemper, Stephan
  Wenninger, Jasmin Koller, Yuhan Zhang, Mario Botsch, Olga Sorkine-Hornung},
    journal={arXiv preprint arXiv:2405.17609},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.17609},
    primaryClass={cs.CV cs.GR}
}