Deep Image-Based Illumination Harmonization

Zhongyun Bao, Chengjiang Long, Gang Fu, Daquan Liu, Yuanzhen Li, Jiaming Wu, Chunxia Xiao; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022, pp. 18542-18551


Integrating a foreground object into a background scenewith illumination harmonization is an important but chal-lenging task in computer vision and augmented reality community. Existing methods mainly focus on foreground andbackground appearance consistency or the foreground object shadow generation, which rarely consider global appearance and illumination harmonization. In this paper,we formulate seamless illumination harmonization as anillumination exchange and aggregation problem. Specifi-cally, we firstly apply a physically-based rendering methodto construct a large-scale, high-quality dataset (named IH)for our task, which contains various types of foreground ob-jects and background scenes with different lighting conditions. Then, we propose a deep image-based illuminationharmonization GAN framework named DIH-GAN, whichmakes full use of a multi-scale attention mechanism and illumination exchange strategy to directly infer mapping rela-tionship between the inserted foreground object and the corresponding background scene. Meanwhile, we also use adversarial learning strategy to further refine the illuminationharmonization result. Our method can not only achieve har-monious appearance and illumination for the foregroundobject but also can generate compelling shadow cast bythe foreground object. Comprehensive experiments on bothour IH dataset and real-world images show that our pro-posed DIH-GAN provides a practical and effective solutionfor image-based object illumination harmonization editing,and validate the superiority of our method against state-of-the-art methods.

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@InProceedings{Bao_2022_CVPR, author = {Bao, Zhongyun and Long, Chengjiang and Fu, Gang and Liu, Daquan and Li, Yuanzhen and Wu, Jiaming and Xiao, Chunxia}, title = {Deep Image-Based Illumination Harmonization}, booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}, month = {June}, year = {2022}, pages = {18542-18551} }