How Will It Drape Like? Capturing Fabric Mechanics from Depth Images

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dc.contributor.authorRodriguez-Pardo, Carlosen_US
dc.contributor.authorPrieto-Martín, Melaniaen_US
dc.contributor.authorCasas, Danen_US
dc.contributor.authorGarces, Elenaen_US
dc.contributor.editorMyszkowski, Karolen_US
dc.contributor.editorNiessner, Matthiasen_US
dc.date.accessioned2023-05-03T06:09:58Z
dc.date.available2023-05-03T06:09:58Z
dc.date.issued2023
dc.description.abstractWe 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.en_US
dc.description.number2
dc.description.sectionheadersLearning Deformations and Fluids
dc.description.seriesinformationComputer Graphics Forum
dc.description.volume42
dc.identifier.doi10.1111/cgf.14750
dc.identifier.issn1467-8659
dc.identifier.pages149-160
dc.identifier.pages12 pages
dc.identifier.urihttps://doi.org/10.1111/cgf.14750
dc.identifier.urihttps://diglib.eg.org:443/handle/10.1111/cgf14750
dc.publisherThe Eurographics Association and John Wiley & Sons Ltd.en_US
dc.rightsAttribution 4.0 International License
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectCCS Concepts: Computing methodologies -> Computer vision; Neural networks; Computer graphics
dc.subjectComputing methodologies
dc.subjectComputer vision
dc.subjectNeural networks
dc.subjectComputer graphics
dc.titleHow Will It Drape Like? Capturing Fabric Mechanics from Depth Imagesen_US
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