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

该研究提出了一种基于样本的图像翻译框架，可以从输入的一个不同域的图像（如语义分割掩模、边缘地图或姿态关键点）中综合出真实感风格与样本图像中对应语义对象的一致的合成图像。通过联合学习交叉领域对应关系和图像翻译模型，两个任务相互促进从而可以在弱监督下学习。该方法在几项图像翻译任务中优于最先进的方法，图像风格传神并具有语义一致性。