  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 rendered harmonization dataset
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 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.
