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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.