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arXiv · 2103.17104

Deep Image Harmonization by Bridging the Reality Gap

Junyan Cao·Wenyan Cong·Li Niu·Jianfu Zhang·Liqing Zhang
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Abstract

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.

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Junyan Cao, Wenyan Cong, Li Niu, Jianfu Zhang, Liqing Zhang. 2021-03-31. Deep Image Harmonization by Bridging the Reality Gap. https://arxiv.org/abs/2103.17104

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