  Recent attempts to solve the problem of head reenactment using a single
reference image have shown promising results. However, most of them either
perform poorly in terms of photo-realism, or fail to meet the identity
preservation problem, or do not fully transfer the driving pose and expression.
We propose HeadGAN, a novel system that conditions synthesis on 3D face
representations, which can be extracted from any driving video and adapted to
the facial geometry of any reference image, disentangling identity from
expression. We further improve mouth movements, by utilising audio features as
a complementary input. The 3D face representation enables HeadGAN to be further
used as an efficient method for compression and reconstruction and a tool for
expression and pose editing.
