Abstract. We present a novel learning-based framework for face reenactment. The proposed method, known as ReenactGAN, is capable of
transferring facial movements and expressions from an arbitrary person’s
monocular video input to a target person’s video. Instead of performing
a direct transfer in the pixel space, which could result in structural artifacts, we first map the source face onto a boundary latent space. A
transformer is subsequently used to adapt the source face’s boundary
to the target’s boundary. Finally, a target-specific decoder is used to
generate the reenacted target face. Thanks to the effective and reliable
boundary-based transfer, our method can perform photo-realistic face
reenactment. In addition, ReenactGAN is appealing in that the whole
reenactment process is purely feed-forward, and thus the reenactment
process can run in real-time (30 FPS on one GTX 1080 GPU). Dataset
and model are publicly available on our project page