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