  Image harmonization has been significantly advanced with large-scale
harmonization dataset. However, the current way to build dataset is still
labor-intensive, which adversely affects the extendability of dataset. To
address this problem, we propose to construct a large-scale rendered
harmonization dataset RHHarmony with fewer human efforts to augment the
existing real-world dataset. To leverage both real-world images and rendered
images, we propose a cross-domain harmonization network CharmNet to bridge the
domain gap between two domains. Moreover, we also employ well-designed style
classifiers and losses to facilitate cross-domain knowledge transfer. Extensive
experiments demonstrate the potential of using rendered images for image
harmonization and the effectiveness of our proposed network. Our dataset and
code are available at
https://github.com/bcmi/Rendered_Image_Harmonization_Datasets.
