  In this paper we introduce SMPLicit, a novel generative model to jointly
represent body pose, shape and clothing geometry. In contrast to existing
learning-based approaches that require training specific models for each type
of garment, SMPLicit can represent in a unified manner different garment
topologies (e.g. from sleeveless tops to hoodies and to open jackets), while
controlling other properties like the garment size or tightness/looseness. We
show our model to be applicable to a large variety of garments including
T-shirts, hoodies, jackets, shorts, pants, skirts, shoes and even hair. The
representation flexibility of SMPLicit builds upon an implicit model
conditioned with the SMPL human body parameters and a learnable latent space
which is semantically interpretable and aligned with the clothing attributes.
The proposed model is fully differentiable, allowing for its use into larger
end-to-end trainable systems. In the experimental section, we demonstrate
SMPLicit can be readily used for fitting 3D scans and for 3D reconstruction in
images of dressed people. In both cases we are able to go beyond state of the
art, by retrieving complex garment geometries, handling situations with
multiple clothing layers and providing a tool for easy outfit editing. To
stimulate further research in this direction, we will make our code and model
publicly available at http://www.iri.upc.edu/people/ecorona/smplicit/.
