  This paper studies the task of full generative modelling of realistic images
of humans, guided only by coarse sketch of the pose, while providing control
over the specific instance or type of outfit worn by the user. This is a
difficult problem because input and output domain are very different and direct
image-to-image translation becomes infeasible. We propose an end-to-end
trainable network under the generative adversarial framework, that provides
detailed control over the final appearance while not requiring paired training
data and hence allows us to forgo the challenging problem of fitting 3D poses
to 2D images. The model allows to generate novel samples conditioned on either
an image taken from the target domain or a class label indicating the style of
clothing (e.g., t-shirt). We thoroughly evaluate the architecture and the
contributions of the individual components experimentally. Finally, we show in
a large scale perceptual study that our approach can generate realistic looking
images and that participants struggle in detecting fake images versus real
samples, especially if faces are blurred.
