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Image composition refers to inserting a foreground object into a backgroundimage to obtain a composite image. In this work, we focus on generatingplausible shadows for the inserted foreground object to make the compositeimage more realistic. To supplement the existing small-scale dataset, we createa large-scale dataset called RdSOBA with rendering techniques. Moreover, wedesign a two-stage network named DMASNet with decomposed mask prediction andattentive shadow filling. Specifically, in the first stage, we decompose shadowmask prediction into box prediction and shape prediction. In the second stage,we attend to reference background shadow pixels to fill the foreground shadow.Abundant experiments prove that our DMASNet achieves better visual effects andgeneralizes well to real composite images.

Shadow Generation with Decomposed Mask Prediction and Attentive Shadow Filling