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

Understand

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 deforming autoencoder to disentangle identity and pose representations.