ReenactGAN: Learning to Reenact Faces via Boundary Transfer
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).
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Citation
@inproceedings{wayne2018reenactgan,
author = {Wu, Wayne and Zhang, Yunxuan and Li, Cheng and Qian, Chen and Loy, Chen Change},
title = {ReenactGAN: Learning to Reenact Faces via Boundary Transfer},
booktitle = {ECCV},
month = September,
year = {2018}
}