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As a common image editing operation, image composition aims to combine theforeground from one image and another background image, resulting in acomposite image. However, there are many issues that could make the compositeimages unrealistic. These issues can be summarized as the inconsistency betweenforeground and background, which includes appearance inconsistency (e.g.,incompatible illumination), geometry inconsistency (e.g., unreasonable size),and semantic inconsistency (e.g., mismatched semantic context). Imagecomposition task could be decomposed into multiple sub-tasks, in which eachsub-task targets at one or more issues. Specifically, object placement aims tofind reasonable scale, location, and shape for the foreground. Image blendingaims to address the unnatural boundary between foreground and background. Imageharmonization aims to adjust the illumination statistics of foreground. Shadowgeneration aims to generate plausible shadow for the foreground. Thesesub-tasks can be executed sequentially or parallelly to acquire realisticcomposite images. To the best of our knowledge, there is no previous survey onimage composition. In this paper, we conduct comprehensive survey over thesub-tasks and combinatorial task of image composition. For each one, wesummarize the existing methods, available datasets, and common evaluationmetrics. Datasets and codes for image composition are summarized athttps://github.com/bcmi/Awesome-Image-Composition.

Making Images Real Again: A Comprehensive Survey on Deep Image Composition