GarmentCodeData: A Dataset of 3D Made-to-Measure Garments With Sewing Patterns
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arXiv
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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866913492302299136 |
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| author | Korosteleva, Maria Kesdogan, Timur Levent Kemper, Fabian Wenninger, Stephan Koller, Jasmin Zhang, Yuhan Botsch, Mario Sorkine-Hornung, Olga |
| author_facet | Korosteleva, Maria Kesdogan, Timur Levent Kemper, Fabian Wenninger, Stephan Koller, Jasmin Zhang, Yuhan Botsch, Mario Sorkine-Hornung, Olga |
| contents | 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/ |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_17609 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | GarmentCodeData: A Dataset of 3D Made-to-Measure Garments With Sewing Patterns Korosteleva, Maria Kesdogan, Timur Levent Kemper, Fabian Wenninger, Stephan Koller, Jasmin Zhang, Yuhan Botsch, Mario Sorkine-Hornung, Olga Computer Vision and Pattern Recognition Graphics 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/ |
| title | GarmentCodeData: A Dataset of 3D Made-to-Measure Garments With Sewing Patterns |
| topic | Computer Vision and Pattern Recognition Graphics |
| url | https://arxiv.org/abs/2405.17609 |