SMPLicit: Topology-aware Generative Model for Clothed People

Enric Corona, Albert Pumarola, Gerard Pons-Moll, Francesc Moreno-Noguer

Abstract:

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 Tshirts, 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/.