Deep Image Harmonization by Bridging the Reality Gap

• URL: http://arxiv.org/abs/2103.17104v1
• Date: Wed, 31 Mar 2021 14:19:56 GMT
• Title: Deep Image Harmonization by Bridging the Reality Gap
• Authors: Wenyan Cong, Junyan Cao, Li Niu, Jianfu Zhang, Xuesong Gao, Zhiwei Tang, Liqing Zhang
• Abstract summary: 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.
• Score: 18.86655082192153
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 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.

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