  Video-driven neural face reenactment aims to synthesize realistic facial
images that successfully preserve the identity and appearance of a source face,
while transferring the target head pose and facial expressions. Existing
GAN-based methods suffer from either distortions and visual artifacts or poor
reconstruction quality, i.e., the background and several important appearance
details, such as hair style/color, glasses and accessories, are not faithfully
reconstructed. Recent advances in Diffusion Probabilistic Models (DPMs) enable
the generation of high-quality realistic images. To this end, in this paper we
present DiffusionAct, a novel method that leverages the photo-realistic image
generation of diffusion models to perform neural face reenactment.
Specifically, we propose to control the semantic space of a Diffusion
Autoencoder (DiffAE), in order to edit the facial pose of the input images,
defined as the head pose orientation and the facial expressions. Our method
allows one-shot, self, and cross-subject reenactment, without requiring
subject-specific fine-tuning. We compare against state-of-the-art GAN-,
StyleGAN2-, and diffusion-based methods, showing better or on-par reenactment
performance.
