  This paper introduces ActGAN - a novel end-to-end generative adversarial
network (GAN) for one-shot face reenactment. Given two images, the goal is to
transfer the facial expression of the source actor onto a target person in a
photo-realistic fashion. While existing methods require target identity to be
predefined, we address this problem by introducing a "many-to-many" approach,
which allows arbitrary persons both for source and target without additional
retraining. To this end, we employ the Feature Pyramid Network (FPN) as a core
generator building block - the first application of FPN in face reenactment,
producing finer results. We also introduce a solution to preserve a person's
identity between synthesized and target person by adopting the state-of-the-art
approach in deep face recognition domain. The architecture readily supports
reenactment in different scenarios: "many-to-many", "one-to-one",
"one-to-another" in terms of expression accuracy, identity preservation, and
overall image quality. We demonstrate that ActGAN achieves competitive
performance against recent works concerning visual quality.
