  Recent studies have shown remarkable success in image-to-image translation
for two domains. However, existing approaches have limited scalability and
robustness in handling more than two domains, since different models should be
built independently for every pair of image domains. To address this
limitation, we propose StarGAN, a novel and scalable approach that can perform
image-to-image translations for multiple domains using only a single model.
Such a unified model architecture of StarGAN allows simultaneous training of
multiple datasets with different domains within a single network. This leads to
StarGAN's superior quality of translated images compared to existing models as
well as the novel capability of flexibly translating an input image to any
desired target domain. We empirically demonstrate the effectiveness of our
approach on a facial attribute transfer and a facial expression synthesis
tasks.
