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Recently, image-to-image translation has been made much progress owing to thesuccess of conditional Generative Adversarial Networks (cGANs). And someunpaired methods based on cycle consistency loss such as DualGAN, CycleGAN andDiscoGAN are really popular. However, it's still very challenging fortranslation tasks with the requirement of high-level visual informationconversion, such as photo-to-caricature translation that requires satire,exaggeration, lifelikeness and artistry. We present an approach for learning totranslate faces in the wild from the source photo domain to the targetcaricature domain with different styles, which can also be used for otherhigh-level image-to-image translation tasks. In order to capture globalstructure with local statistics while translation, we design a dual pathwaymodel with one coarse discriminator and one fine discriminator. For generator,we provide one extra perceptual loss in association with adversarial loss andcycle consistency loss to achieve representation learning for two differentdomains. Also the style can be learned by the auxiliary noise input.Experiments on photo-to-caricature translation of faces in the wild showconsiderable performance gain of our proposed method over state-of-the-arttranslation methods as well as its potential real applications.

Unpaired Photo-to-Caricature Translation on Faces in the Wild