ActGAN: Flexible and Efficient One-shot Face Reenactment

• URL: http://arxiv.org/abs/2003.13840v1
• Date: Mon, 30 Mar 2020 22:03:16 GMT
• Title: ActGAN: Flexible and Efficient One-shot Face Reenactment
• Authors: Ivan Kosarevych, Marian Petruk, Markian Kostiv, Orest Kupyn, Mykola Maksymenko, Volodymyr Budzan
• Abstract summary: ActGAN is a novel end-to-end generative adversarial network (GAN) for one-shot face reenactment. We introduce a "many-to-many" approach, which allows arbitrary persons both for source and target without additional retraining. 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.
• Score: 1.8431600219151503
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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