  Our ability to sample realistic natural images, particularly faces, has
advanced by leaps and bounds in recent years, yet our ability to exert
fine-tuned control over the generative process has lagged behind. If this new
technology is to find practical uses, we need to achieve a level of control
over generative networks which, without sacrificing realism, is on par with
that seen in computer graphics and character animation. To this end we propose
ConfigNet, a neural face model that allows for controlling individual aspects
of output images in semantically meaningful ways and that is a significant step
on the path towards finely-controllable neural rendering. ConfigNet is trained
on real face images as well as synthetic face renders. Our novel method uses
synthetic data to factorize the latent space into elements that correspond to
the inputs of a traditional rendering pipeline, separating aspects such as head
pose, facial expression, hair style, illumination, and many others which are
very hard to annotate in real data. The real images, which are presented to the
network without labels, extend the variety of the generated images and
encourage realism. Finally, we propose an evaluation criterion using an
attribute detection network combined with a user study and demonstrate
state-of-the-art individual control over attributes in the output images.
