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 monocular video input of an arbitrary person to
a target person. 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 boundary
of source face to the boundary of target face. 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 will be
publicly available at
this https URL

提出了一种名为 ReenactGAN 的基于学习的框架，可从单眼视频输入转移人脸运动和表情到目标人物。此方法将源脸映射到边界潜空间，使用变形器进行调整，最终生成重现的目标面孔，这一过程可以在实时（30 FPS 的 GTX 1080 GPU 上）中执行。