Shadow generation with decomposed mask prediction and attentive shadow filling

Xinhao Tao, Junyan Cao, Yan Hong, Li Niu

Abstract:

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 flling. Specifcally, in the frst stage, we decompose
shadow mask prediction into box prediction and shape prediction. In the second stage, we attend to reference background shadow pixels to fll the foreground shadow. Abundant experiments prove that our DMASNet achieves better
visual effects and generalizes well to real composite images.