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We present Multi-Garment Network (MGN), a method to predict body shape andclothing, 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 ofcontrol when compared to single mesh or voxel representations of shape. Ourmodel allows to predict garment geometry, relate it to the body shape, andtransfer it to new body shapes and poses. To train MGN, we leverage a digitalwardrobe containing 712 digital garments in correspondence, obtained with anovel method to register a set of clothing templates to a dataset of real 3Dscans of people in different clothing and poses. Garments from the digitalwardrobe, or predicted by MGN, can be used to dress any body shape in arbitraryposes. We will make publicly available the digital wardrobe, the MGN model, andcode to dress SMPL with the garments.

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