Multi-Garment Net: Learning to Dress 3D People from Images
This work addresses the challenge of realistic 3D human dressing for applications in animation and virtual reality, representing an incremental advance in garment modeling.
The paper tackles the problem of predicting body shape and clothing from video frames by introducing Multi-Garment Network (MGN), which uses a digital wardrobe of 712 garments to enable garment geometry prediction and transfer to new body shapes and poses.
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.