HyperReenact: One-Shot Reenactment via Jointly Learning to Refine and
  Retarget Faces
Stella Bounareli Christos Tzelepis Vasileios Argyriou Ioannis Patras Georgios Tzimiropoulos
In this paper, we present our method for neural face reenactment, called
HyperReenact, that aims to generate realistic talking head images of a source
identity, driven by a target facial pose. Existing state-of-the-art face
reenactment methods train controllable generative models that learn to
synthesize realistic facial images, yet producing reenacted faces that are
prone to significant visual artifacts, especially under the challenging
condition of extreme head pose changes, or requiring expensive few-shot
fine-tuning to better preserve the source identity characteristics. We propose
to address these limitations by leveraging the photorealistic generation
ability and the disentangled properties of a pretrained StyleGAN2 generator, by
first inverting the real images into its latent space and then using a
hypernetwork to perform: (i) refinement of the source identity characteristics
and (ii) facial pose re-targeting, eliminating this way the dependence on
external editing methods that typically produce artifacts. Our method operates
under the one-shot setting (i.e., using a single source frame) and allows for
cross-subject reenactment, without requiring any subject-specific fine-tuning.
We compare our method both quantitatively and qualitatively against several
state-of-the-art techniques on the standard benchmarks of VoxCeleb1 and
VoxCeleb2, demonstrating the superiority of our approach in producing
artifact-free images, exhibiting remarkable robustness even under extreme head
pose changes. We make the code and the pretrained models publicly available at:
https://github.com/StelaBou/HyperReenact .