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Conference Paper

Multi-Garment Net: Learning to Dress 3D People from Images

MPS-Authors
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Bhatnagar,  Bharat Lal
Computer Vision and Machine Learning, MPI for Informatics, Max Planck Society;

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Tiwari,  Garvita
Computer Vision and Machine Learning, MPI for Informatics, Max Planck Society;

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Theobalt,  Christian
Computer Graphics, MPI for Informatics, Max Planck Society;

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Pons-Moll,  Gerard
Computer Vision and Machine Learning, MPI for Informatics, Max Planck Society;

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Citation

Bhatnagar, B. L., Tiwari, G., Theobalt, C., & Pons-Moll, G. (in press). Multi-Garment Net: Learning to Dress 3D People from Images. In ICCV 2019. Piscataway, NJ: IEEE.


Cite as: http://hdl.handle.net/21.11116/0000-0004-89E8-C
Abstract
We present Multi-Garment Network (MGN), a method to predict body shape and clothing, layered on top of the SMPL model from a few frames (1-8) of a video. Several experiments demonstrate that this representation allows higher level of control when compared to single mesh or voxel representations of shape. Our model allows to predict garment geometry, relate it to the body shape, and transfer it to new body shapes and poses. To train MGN, we leverage a digital wardrobe containing 712 digital garments in correspondence, obtained with a novel method to register a set of clothing templates to a dataset of real 3D scans of people in different clothing and poses. Garments from the digital wardrobe, or predicted by MGN, can be used to dress any body shape in arbitrary poses. We will make publicly available the digital wardrobe, the MGN model, and code to dress SMPL with the garments.