  Image composition refers to inserting a foreground object into a background
image to obtain a composite image. In this work, we focus on generating
plausible shadows for the inserted foreground object to make the composite
image more realistic. To supplement the existing small-scale dataset, we create
a large-scale dataset called RdSOBA with rendering techniques. Moreover, we
design a two-stage network named DMASNet with decomposed mask prediction and
attentive shadow filling. Specifically, in the first stage, we decompose shadow
mask 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 and
generalizes well to real composite images.
