  We present the first image-based generative model of people in clothing for
the full body. We sidestep the commonly used complex graphics rendering
pipeline and the need for high-quality 3D scans of dressed people. Instead, we
learn generative models from a large image database. The main challenge is to
cope with the high variance in human pose, shape and appearance. For this
reason, pure image-based approaches have not been considered so far. We show
that this challenge can be overcome by splitting the generating process in two
parts. First, we learn to generate a semantic segmentation of the body and
clothing. Second, we learn a conditional model on the resulting segments that
creates realistic images. The full model is differentiable and can be
conditioned on pose, shape or color. The result are samples of people in
different clothing items and styles. The proposed model can generate entirely
new people with realistic clothing. In several experiments we present
encouraging results that suggest an entirely data-driven approach to people
generation is possible.
