We present a new technique to automatically generate procedural representations of yarn geometry. Based on geometric measurements of physical yarn samples (a), our approach fits statistical representations of fiber geometry that closely match reality (b). The four yarns in (a, b) from top to bottom are cotton, rayon, silk, and polyester. Our fitted models can populate realistic fiber-level details into yarn-based fabric models (generated using textile design software or physically-based yarn simulation) to significantly improve the quality of the rendered fabrics (c-top vs. c-middle (ours)). Our procedural models carry high-level synthetic information (e.g., twisting and hairiness) which offers easy editability (c-bottom).
AbstractFabrics play a significant role in many applications in design, prototyping, and entertainment. Recent fiber-based models capture the rich visual appearance of fabrics, but are too onerous to design and edit. Yarn-based procedural models are powerful and convenient, but too regular and not realistic enough in appearance. In this paper, we introduce an automatic fitting approach to create high-quality procedural yarn models of fabrics with fiber-level details. We fit CT data to procedural models to automatically recover a full range of parameters, and augment the models with a measurement-based model of flyaway fibers. We validate our fabric models against CT measurements and photographs, and demonstrate the utility of this approach for fabric modeling and editing.