  In this paper, we present our framework for neural face/head reenactment
whose goal is to transfer the 3D head orientation and expression of a target
face to a source face. Previous methods focus on learning embedding networks
for identity and head pose/expression disentanglement which proves to be a
rather hard task, degrading the quality of the generated images. We take a
different approach, bypassing the training of such networks, by using
(fine-tuned) pre-trained GANs which have been shown capable of producing
high-quality facial images. Because GANs are characterized by weak
controllability, the core of our approach is a method to discover which
directions in latent GAN space are responsible for controlling head pose and
expression variations. We present a simple pipeline to learn such directions
with the aid of a 3D shape model which, by construction, inherently captures
disentangled directions for head pose, identity, and expression. Moreover, we
show that by embedding real images in the GAN latent space, our method can be
successfully used for the reenactment of real-world faces. Our method features
several favorable properties including using a single source image (one-shot)
and enabling cross-person reenactment. Extensive qualitative and quantitative
results show that our approach typically produces reenacted faces of notably
higher quality than those produced by state-of-the-art methods for the standard
benchmarks of VoxCeleb1 & 2.
