Dressing 3D Humans using a Conditional Mesh-VAE-GAN

Three-dimensional human body models are widely used in the analysis of human pose and motion. Existing models, however, are learned from minimally-clothed humans and thus do not capture the complexity of dressed humans in common images and videos. To address this, we learn a generative 3D mesh model of clothing from 3D scans of people with varying pose. Going beyond previous work, our generative model is conditioned on different clothing types, giving the ability to dress different body shapes in a variety of clothing. To do so, we train a conditional Mesh-VAE-GAN on clothing displacements from a 3D SMPL body model. This generative clothing model enables us to sample various types of clothing, in novel poses, on top of SMPL. With a focus on clothing geometry, the model captures both global shape and local structure, effectively extending the SMPL model to add clothing. To our knowledge, this is the first conditional VAE-GAN that works on 3D meshes. For clothing specifically, it is the first such model that directly dresses 3D human body meshes and generalizes to different poses.