  We present a general framework for exemplar-based image translation, which
synthesizes a photo-realistic image from the input in a distinct domain (e.g.,
semantic segmentation mask, or edge map, or pose keypoints), given an exemplar
image. The output has the style (e.g., color, texture) in consistency with the
semantically corresponding objects in the exemplar. We propose to jointly learn
the crossdomain correspondence and the image translation, where both tasks
facilitate each other and thus can be learned with weak supervision. The images
from distinct domains are first aligned to an intermediate domain where dense
correspondence is established. Then, the network synthesizes images based on
the appearance of semantically corresponding patches in the exemplar. We
demonstrate the effectiveness of our approach in several image translation
tasks. Our method is superior to state-of-the-art methods in terms of image
quality significantly, with the image style faithful to the exemplar with
semantic consistency. Moreover, we show the utility of our method for several
applications
