How Will It Drape Like? Capturing Fabric Mechanics from Depth Images
Abstract
We propose a method to estimate the mechanical parameters of fabrics using a casual capture setup with a depth camera. Our approach enables to create mechanically-correct digital representations of real-world textile materials, which is a fundamental step for many interactive design and engineering applications. As opposed to existing capture methods, which typically require expensive setups, video sequences, or manual intervention, our solution can capture at scale, is agnostic to the optical appearance of the textile, and facilitates fabric arrangement by non-expert operators. To this end, we propose a sim-to-real strategy to train a learning-based framework that can take as input one or multiple images and outputs a full set of mechanical parameters. Thanks to carefully designed data augmentation and transfer learning protocols, our solution generalizes to real images despite being trained only on synthetic data, hence successfully closing the sim-to-real loop. Key in our work is to demonstrate that evaluating the regression accuracy based on the similarity at parameter space leads to an inaccurate distances that do not match the human perception. To overcome this, we propose a novel metric for fabric drape similarity that operates on the image domain instead on the parameter space, allowing us to evaluate our estimation within the context of a similarity rank. We show that out metric correlates with human judgments about the perception of drape similarity, and that our model predictions produce perceptually accurate results compared to the ground truth parameters.
BibTeX
@article {10.1111:cgf.14750,
journal = {Computer Graphics Forum},
title = {{How Will It Drape Like? Capturing Fabric Mechanics from Depth Images}},
author = {Rodriguez-Pardo, Carlos and Prieto-Martín, Melania and Casas, Dan and Garces, Elena},
year = {2023},
publisher = {The Eurographics Association and John Wiley & Sons Ltd.},
ISSN = {1467-8659},
DOI = {10.1111/cgf.14750}
}
journal = {Computer Graphics Forum},
title = {{How Will It Drape Like? Capturing Fabric Mechanics from Depth Images}},
author = {Rodriguez-Pardo, Carlos and Prieto-Martín, Melania and Casas, Dan and Garces, Elena},
year = {2023},
publisher = {The Eurographics Association and John Wiley & Sons Ltd.},
ISSN = {1467-8659},
DOI = {10.1111/cgf.14750}
}
Collections
Except where otherwise noted, this item's license is described as Attribution 4.0 International License
Related items
Showing items related by title, author, creator and subject.
-
Rational Bézier Guarding
Khanteimouri, Payam; Mandad, Manish; Campen, Marcel (The Eurographics Association and John Wiley & Sons Ltd., 2022)We present a reliable method to generate planar meshes of nonlinear rational triangular elements. The elements are guaranteed to be valid, i.e. defined by injective rational functions. The mesh is guaranteed to conform ... -
VA + Embeddings STAR: A State-of-the-Art Report on the Use of Embeddings in Visual Analytics
Huang, Zeyang; Witschard, Daniel; Kucher, Kostiantyn; Kerren, Andreas (The Eurographics Association and John Wiley & Sons Ltd., 2023)Over the past years, an increasing number of publications in information visualization, especially within the field of visual analytics, have mentioned the term ''embedding'' when describing the computational approach. ... -
Teaching Game Programming in an Upper-level Computing Course Through the Development of a C++ Framework and Middleware
Hooper, Steffan; Wünsche, Burkhard C.; Denny, Paul; Luxton-Reilly, Andrew (The Eurographics Association, 2024)The game development industry has a programming skills shortage, with industry surveys often ranking game programming as the top skill-in-demand across small, mid-sized, and large triple-A (AAA) game studios. C++ programming ...