Realistic Face Reenactment via Self-Supervised Disentangling of Identity and Pose

@article{Zeng2020RealisticFR,
  title={Realistic Face Reenactment via Self-Supervised Disentangling of Identity and Pose},
  author={Xianfang Zeng and Yusu Pan and Mengmeng Wang and Jiangning Zhang and Yong Liu},
  journal={ArXiv},
  year={2020},
  volume={abs/2003.12957}
}
Recent works have shown how realistic talking face images can be obtained under the supervision of geometry guidance, e.g., facial landmark or boundary. To alleviate the demand for manual annotations, in this paper, we propose a novel self-supervised hybrid model (DAE-GAN) that learns how to reenact face naturally given large amounts of unlabeled videos. Our approach combines two deforming autoencoders with the latest advances in the conditional generation. On the one hand, we adopt the… 

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