GarmentCodeData: A Dataset of 3D Made-to-Measure Garments With Sewing Patterns

• URL: http://arxiv.org/abs/2405.17609v3
• Date: Thu, 5 Sep 2024 14:00:27 GMT
• Title: GarmentCodeData: A Dataset of 3D Made-to-Measure Garments With Sewing Patterns
• Authors: Maria Korosteleva, Timur Levent Kesdogan, Fabian Kemper, Stephan Wenninger, Jasmin Koller, Yuhan Zhang, Mario Botsch, Olga Sorkine-Hornung,
• Abstract summary: We present the first large-scale synthetic dataset of 3D made-to-measure garments with sewing patterns. GarmentCodeData contains 115,000 data points that cover a variety of designs in many common garment categories. We propose an automatic, open-source 3D garment draping pipeline based on a fast XPBD simulator.
• Score: 18.513707884523072
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: 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/

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