PERGAMO: Personalized 3D Garments from Monocular Video
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 development; and simulation-to-real gap, which i...
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Main Authors | , , |
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Format | Journal Article |
Language | English |
Published |
26.10.2022
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Subjects | |
Online Access | Get full text |
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Summary: | 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 development; 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
behaviour, and generalizes to unseen body motions extracted from motion capture
dataset. |
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DOI: | 10.48550/arxiv.2210.15040 |