Deep Face Swapping via Cross-Identity Adversarial Training.

Generative Adversarial Networks (GANs) have shown promising improvements in face synthesis and image manipulation. However, it remains difficult to swap the faces in videos with a specific target. The most well-known face swapping method, Deepfakes, focuses on reconstructing the face image with auto-encoder while paying less attention to the identity gap between the source and target faces, which causes the swapped face looks like both the source face and the target face. In this work, we propose to incorporate cross-identity adversarial training mechanism for highly photo-realistic face swapping. Specifically, we introduce corresponding discriminator to faithfully try to distinguish the swapped faces, reconstructed faces and real faces in the training process. In addition, attention mechanism is applied to make our network robust to variation of illumination. Comprehensive experiments are conducted to demonstrate the superiority of our method over baseline models in quantitative and qualitative fashion.