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.

该研究介绍了一种名为 MGN 的多服装网络模型，利用 SMPL 模型，通过视频中几帧图像（1-8）预测身体的形状及所穿服装，该模型能够更好的控制身形和服装的关系。为了训练 MGN，研究人员利用一个数字化的衣柜，该衣柜中包含了 712 件数字化服装模板以及真实人物在不同姿势、身着不同服装时的 3D 扫描数据。该数字化衣柜、MGN 模型以及 SMPL 模型的代码均可公开获取。