Estimation of Yarn-Level Simulation Models for Production Fabrics [SIGGRAPH 2022 Presentation]

This is the presentation for the SIGGRAPH 2022 paper "Estimation of Yarn-Level Simulation Models for Production Fabrics" by Georg Sperl, Rosa M. Sánchez-Banderas, Manwen Li, Chris Wojtan and Miguel A. Otaduy.

Project Page:
https://mslab.es/projects/YarnLevelFabrics/

Abstract:
This paper introduces a methodology for inverse-modeling of yarn-level mechanics of cloth, based on the mechanical response of fabrics in the real world. We compiled a database from physical tests of several different knitted fabrics used in the textile industry. These data span different types of complex knit patterns, yarn compositions, and fabric finishes, and the results demonstrate diverse physical properties like stiffness, nonlinearity, and anisotropy.
We then develop a system for approximating these mechanical responses with yarn-level cloth simulation. To do so, we introduce an efficient pipeline for converting between fabric-level data and yarn-level simulation, including a novel swatch-level approximation for speeding up computation, and some small-but-necessary extensions to yarn-level models used in computer graphics.

Chapters:
0:00 Introduction
2:46 Overview
3:37 Production Fabric Dataset
5:47 Inverse Design of Yarn Parameters
9:49 Results
13:53 Conclusion

Видео Estimation of Yarn-Level Simulation Models for Production Fabrics [SIGGRAPH 2022 Presentation] канала Visual Computing@IST Austria