  Exemplar-based image translation refers to the task of generating images with
the desired style, while conditioning on certain input image. Most of the
current methods learn the correspondence between two input domains and lack the
mining of information within the domains. In this paper, we propose a more
general learning approach by considering two domain features as a whole and
learning both inter-domain correspondence and intra-domain potential
information interactions. Specifically, we propose a Cross-domain Feature
Fusion Transformer (CFFT) to learn inter- and intra-domain feature fusion.
Based on CFFT, the proposed CFFT-GAN works well on exemplar-based image
translation. Moreover, CFFT-GAN is able to decouple and fuse features from
multiple domains by cascading CFFT modules. We conduct rich quantitative and
qualitative experiments on several image translation tasks, and the results
demonstrate the superiority of our approach compared to state-of-the-art
methods. Ablation studies show the importance of our proposed CFFT. Application
experimental results reflect the potential of our method.
