  This paper is on face/head reenactment where the goal is to transfer the
facial pose (3D head orientation and expression) of a target face to a source
face. Previous methods focus on learning embedding networks for identity and
pose 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
facial pose and expression variations. We present a simple pipeline to learn
such directions with the aid of a 3D shape model which, by construction,
already captures disentangled directions for facial 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. Our
qualitative and quantitative results show that our approach often produces
reenacted faces of significantly higher quality than those produced by
state-of-the-art methods for the standard benchmarks of VoxCeleb1 & 2. Source
code is available at:
https://github.com/StelaBou/stylegan_directions_face_reenactment
