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GarmentCode: Programming Parametric Sewing Patterns

Authors :
Korosteleva, Maria
Sorkine-Hornung, Olga
Source :
ACM Trans. Graph. 42, 6, Article 197 (December 2023), 15 pages
Publication Year :
2023

Abstract

Garment modeling is an essential task of the global apparel industry and a core part of digital human modeling. Realistic representation of garments with valid sewing patterns is key to their accurate digital simulation and eventual fabrication. However, little-to-no computational tools provide support for bridging the gap between high-level construction goals and low-level editing of pattern geometry, e.g., combining or switching garment elements, semantic editing, or design exploration that maintains the validity of a sewing pattern. We suggest the first DSL for garment modeling -- GarmentCode -- that applies principles of object-oriented programming to garment construction and allows designing sewing patterns in a hierarchical, component-oriented manner. The programming-based paradigm naturally provides unique advantages of component abstraction, algorithmic manipulation, and free-form design parametrization. We additionally support the construction process by automating typical low-level tasks like placing a dart at a desired location. In our prototype garment configurator, users can manipulate meaningful design parameters and body measurements, while the construction of pattern geometry is handled by garment programs implemented with GarmentCode. Our configurator enables the free exploration of rich design spaces and the creation of garments using interchangeable, parameterized components. We showcase our approach by producing a variety of garment designs and retargeting them to different body shapes using our configurator. Project page: https://igl.ethz.ch/projects/garmentcode/<br />Comment: Presented at SIGGRAPH Asia 2023

Subjects

Subjects :
Computer Science - Graphics

Details

Database :
arXiv
Journal :
ACM Trans. Graph. 42, 6, Article 197 (December 2023), 15 pages
Publication Type :
Report
Accession number :
edsarx.2306.03642
Document Type :
Working Paper
Full Text :
https://doi.org/10.1145/3618351