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

Bharat Lal Bhatnagar, Garvita Tiwari, Christian Theobalt, Gerard Pons-Moll

Abstract:

We present Multi-Garment Network (MGN), a method
to predict body shape and clothing, layered on top of the
SMPL [40] 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.