  Recently, image-to-image translation has been made much progress owing to the
success of conditional Generative Adversarial Networks (cGANs). And some
unpaired methods based on cycle consistency loss such as DualGAN, CycleGAN and
DiscoGAN are really popular. However, it's still very challenging for
translation tasks with the requirement of high-level visual information
conversion, such as photo-to-caricature translation that requires satire,
exaggeration, lifelikeness and artistry. We present an approach for learning to
translate faces in the wild from the source photo domain to the target
caricature domain with different styles, which can also be used for other
high-level image-to-image translation tasks. In order to capture global
structure with local statistics while translation, we design a dual pathway
model with one coarse discriminator and one fine discriminator. For generator,
we provide one extra perceptual loss in association with adversarial loss and
cycle consistency loss to achieve representation learning for two different
domains. Also the style can be learned by the auxiliary noise input.
Experiments on photo-to-caricature translation of faces in the wild show
considerable performance gain of our proposed method over state-of-the-art
translation methods as well as its potential real applications.
