  Image composition targets at inserting a foreground object into a background
image. Most previous image composition methods focus on adjusting the
foreground to make it compatible with background while ignoring the shadow
effect of foreground on the background. In this work, we focus on generating
plausible shadow for the foreground object in the composite image. First, we
contribute a real-world shadow generation dataset DESOBA by generating
synthetic composite images based on paired real images and deshadowed images.
Then, we propose a novel shadow generation network SGRNet, which consists of a
shadow mask prediction stage and a shadow filling stage. In the shadow mask
prediction stage, foreground and background information are thoroughly
interacted to generate foreground shadow mask. In the shadow filling stage,
shadow parameters are predicted to fill the shadow area. Extensive experiments
on our DESOBA dataset and real composite images demonstrate the effectiveness
of our proposed method. Our dataset and code are available at
https://github.com/bcmi/Object-Shadow-Generation-Dataset-DESOBA.
