  Deep generative models can synthesize photorealistic images of human faces
with novel identities. However, a key challenge to the wide applicability of
such techniques is to provide independent control over semantically meaningful
parameters: appearance, head pose, face shape, and facial expressions. In this
paper, we propose VariTex - to the best of our knowledge the first method that
learns a variational latent feature space of neural face textures, which allows
sampling of novel identities. We combine this generative model with a
parametric face model and gain explicit control over head pose and facial
expressions. To generate complete images of human heads, we propose an additive
decoder that adds plausible details such as hair. A novel training scheme
enforces a pose-independent latent space and in consequence, allows learning a
one-to-many mapping between latent codes and pose-conditioned exterior regions.
The resulting method can generate geometrically consistent images of novel
identities under fine-grained control over head pose, face shape, and facial
expressions. This facilitates a broad range of downstream tasks, like sampling
novel identities, changing the head pose, expression transfer, and more. Code
and models are available for research on https://mcbuehler.github.io/VariTex.
