PERGAMO: Personalized 3D Garments from Monocular Video

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Date
2022
Journal Title
Journal ISSN
Volume Title
Publisher
The Eurographics Association and John Wiley & Sons Ltd.
Abstract
Clothing plays a fundamental role in digital humans. Current approaches to animate 3D garments are mostly based on realistic physics simulation, however, they typically suffer from two main issues: high computational run-time cost, which hinders their deployment; and simulation-to-real gap, which impedes the synthesis of specific real-world cloth samples. To circumvent both issues we propose PERGAMO, a data-driven approach to learn a deformable model for 3D garments from monocular images. To this end, we first introduce a novel method to reconstruct the 3D geometry of garments from a single image, and use it to build a dataset of clothing from monocular videos. We use these 3D reconstructions to train a regression model that accurately predicts how the garment deforms as a function of the underlying body pose. We show that our method is capable of producing garment animations that match the real-world behavior, and generalizes to unseen body motions extracted from motion capture dataset.
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CCS Concepts: Computing methodologies --> Computer graphics; Neural networks

        
@article{
10.1111:cgf.14644
, journal = {Computer Graphics Forum}, title = {{
PERGAMO: Personalized 3D Garments from Monocular Video
}}, author = {
Casado-Elvira, Andrés
and
Comino Trinidad, Marc
and
Casas, Dan
}, year = {
2022
}, publisher = {
The Eurographics Association and John Wiley & Sons Ltd.
}, ISSN = {
1467-8659
}, DOI = {
10.1111/cgf.14644
} }
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