  Facial caricature is an art form of drawing faces in an exaggerated way to
convey humor or sarcasm. In this paper, we propose the first Generative
Adversarial Network (GAN) for unpaired photo-to-caricature translation, which
we call "CariGANs". It explicitly models geometric exaggeration and appearance
stylization using two components: CariGeoGAN, which only models the
geometry-to-geometry transformation from face photos to caricatures, and
CariStyGAN, which transfers the style appearance from caricatures to face
photos without any geometry deformation. In this way, a difficult cross-domain
translation problem is decoupled into two easier tasks. The perceptual study
shows that caricatures generated by our CariGANs are closer to the hand-drawn
ones, and at the same time better persevere the identity, compared to
state-of-the-art methods. Moreover, our CariGANs allow users to control the
shape exaggeration degree and change the color/texture style by tuning the
parameters or giving an example caricature.
