  Image-to-image translation (I2I) is defined as a computer vision task where
the aim is to transfer images in a source domain to a target domain with
minimal loss or alteration of the content representations. Major progress has
been made since I2I was proposed with the invention of a variety of
revolutionary generative models. Among them, GAN-based models perform
exceptionally well as they are mostly tailor-made for specific domains or
tasks. However, few works proposed a tailor-made method for the artistic
domain. In this project, I propose the Semantic-aware Mask CycleGAN
(SMCycleGAN) architecture which can translate artistic portraits to
photo-realistic visualizations. This model can generate realistic human
portraits by feeding the discriminators semantically masked fake samples, thus
enforcing them to make discriminative decisions with partial information so
that the generators can be optimized to synthesize more realistic human
portraits instead of increasing the similarity of other irrelevant components,
such as the background. Experiments have shown that the SMCycleGAN generate
images with significantly increased realism and minimal loss of content
representations.
