Estimation of Yarn-Level Simulation Models for Production Fabrics

MSLab Project Page

Publication

ACM Transactions on Graphics (Siggraph 2022)

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.

Submission Video

Full Presentation

Resources

• Paper (7 MB)
• Supplemental Document (10 MB)
• Dataset (136 MB)
• Dataset Preview

Citation

@article{sperl2022eylsmpf,
    author    = {Sperl, Georg and Sánchez-Banderas,  Rosa M. and Li, Manwen and Wojtan, Chris and Otaduy, Miguel A.},
    title     = {Estimation of Yarn-Level Simulation Models for Production Fabrics},
    journal   = {ACM Transactions on Graphics (TOG)},
    number    = {4},
    volume    = {41},
    year      = {2022},
    publisher = {ACM}
}