GarmentCodeData: A Dataset of 3D Made-to-Measure Garments With Sewing Patterns
| Autor: | Korosteleva, Maria, Kesdogan, Timur Levent, Kemper, Fabian, Wenninger, Stephan, Koller, Jasmin, Zhang, Yuhan, Botsch, Mario, Sorkine-Hornung, Olga |
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| Rok vydání: | 2024 |
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| Druh dokumentu: | Working Paper |
| Popis: | 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/ Comment: Accepted to ECCV 2024. Sept 4th, 2024: release of GarmentCodeData(v2) |
| Databáze: | arXiv |
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