  Deep generative models have recently demonstrated the ability to synthesize
photorealistic images of human faces with novel identities. 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 images of complete human
heads, we propose an additive decoder that generates plausible additional
details such as hair. A novel training scheme enforces a pose independent
latent space and in consequence, allows learning of a one-to-many mapping
between latent codes and pose-conditioned exterior regions. The resulting
method can generate geometrically consistent images of novel identities
allowing fine-grained control over head pose, face shape, and facial
expressions, facilitating a broad range of downstream tasks, like sampling
novel identities, re-posing, expression transfer, and more.
