Affiliation:
1. University of Technology Sydney, Australia
2. ETH Zurich, Switzerland
Abstract
We propose a method for computing a sewing pattern of a given 3D garment model. Our algorithm segments an input 3D garment shape into patches and computes their 2D parameterization, resulting in pattern pieces that can be cut out of fabric and sewn together to manufacture the garment. Unlike the general state-of-the-art approaches for surface cutting and flattening, our method explicitly targets garment fabrication. It accounts for the unique properties and constraints of tailoring, such as seam symmetry, the usage of darts, fabric grain alignment, and a flattening distortion measure that models woven fabric deformation, respecting its anisotropic behavior. We bootstrap a recent patch layout approach developed for quadrilateral remeshing and adapt it to the purpose of computational pattern making, ensuring that the deformation of each pattern piece stays within prescribed bounds of cloth stress. While our algorithm can automatically produce the sewing patterns, it is fast enough to admit user input to creatively iterate on the pattern design. Our method can take several target poses of the 3D garment into account and integrate them into the sewing pattern design. We demonstrate results on both skintight and loose garments, showcasing the versatile application possibilities of our approach.
Funder
European Research Council
Publisher
Association for Computing Machinery (ACM)
Subject
Computer Graphics and Computer-Aided Design
Reference75 articles.
1. Thiemo Alldieck Hongyi Xu and Cristian Sminchisescu. 2021. imGHUM: Implicit Generative Models of 3D Human Shape and Articulated Pose. arXiv:2108.10842 https://arxiv.org/abs/2108.10842 Thiemo Alldieck Hongyi Xu and Cristian Sminchisescu. 2021. imGHUM: Implicit Generative Models of 3D Human Shape and Articulated Pose. arXiv:2108.10842 https://arxiv.org/abs/2108.10842
2. Estimating Garment Patterns from Static Scan Data
3. Physics-driven pattern adjustment for direct 3D garment editing
4. Developable Approximation via Gauss Image Thinning
5. Federica Bogo , Javier Romero , Matthew Loper , and Michael J. Black . 2014. FAUST: Dataset and evaluation for 3D mesh registration . In Proceedings IEEE Conf. on Computer Vision and Pattern Recognition (CVPR). IEEE , Piscataway, NJ, USA, 3794--3801. Federica Bogo, Javier Romero, Matthew Loper, and Michael J. Black. 2014. FAUST: Dataset and evaluation for 3D mesh registration. In Proceedings IEEE Conf. on Computer Vision and Pattern Recognition (CVPR). IEEE, Piscataway, NJ, USA, 3794--3801.
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