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Recent studies have used GAN to transfer expressions between human faces.However, existing models have many flaws: relying on emotion labels, lackingcontinuous expressions, and failing to capture the expression details. Toaddress these limitations, we propose a novel CycleGAN- and InfoGAN-basednetwork called 2 Cycles Expression Transfer GAN (2CET-GAN), which can learncontinuous expression transfer without using emotion labels. The experimentshows our network can generate diverse and high-quality expressions and cangeneralize to unknown identities. To the best of our knowledge, we are amongthe first to successfully use an unsupervised approach to disentangleexpression representation from identities at the pixel level.

2CET-GAN: Pixel-Level GAN Model for Human Facial Expression Transfer