Practical Method to Estimate Fabric Mechanics from Metadata
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Date
2024
Journal Title
Journal ISSN
Volume Title
Publisher
The Eurographics Association and John Wiley & Sons Ltd.
Abstract
Estimating fabric mechanical properties is crucial to create realistic digital twins. Existing methods typically require testing physical fabric samples with expensive devices or cumbersome capture setups. In this work, we propose a method to estimate fabric mechanics just from known manufacturer metadata such as the fabric family, the density, the composition, and the thickness. Further, to alleviate the need to know the fabric family –which might be ambiguous or unknown for nonspecialists– we propose an end-to-end neural method that works with planar images of the textile as input. We evaluate our methods using extensive tests that include the industry standard Cusick and demonstrate that both of them produce drapes that strongly correlate with the ground truth estimates provided by lab equipment. Our method is the first to propose such a simple capture method for mechanical properties outperforming other methods that require testing the fabric in specific setups.
Description
CCS Concepts: Computing methodologies -> Computer vision; Neural networks; Computer graphics
@article{10.1111:cgf.15029,
journal = {Computer Graphics Forum},
title = {{Practical Method to Estimate Fabric Mechanics from Metadata}},
author = {Dominguez-Elvira, Henar and Nicás, Alicia and Cirio, Gabriel and Rodríguez, Alejandro and Garces, Elena},
year = {2024},
publisher = {The Eurographics Association and John Wiley & Sons Ltd.},
ISSN = {1467-8659},
DOI = {10.1111/cgf.15029}
}