  Dealing with the inconsistency between a foreground object and a background
image is a challenging task in high-fidelity image composition.
State-of-the-art methods strive to harmonize the composed image by adapting the
style of foreground objects to be compatible with the background image, whereas
the potential shadow of foreground objects within the composed image which is
critical to the composition realism is largely neglected. In this paper, we
propose an Adversarial Image Composition Net (AIC-Net) that achieves realistic
image composition by considering potential shadows that the foreground object
projects in the composed image. A novel branched generation mechanism is
proposed, which disentangles the generation of shadows and the transfer of
foreground styles for optimal accomplishment of the two tasks simultaneously. A
differentiable spatial transformation module is designed which bridges the
local harmonization and the global harmonization to achieve their joint
optimization effectively. Extensive experiments on pedestrian and car
composition tasks show that the proposed AIC-Net achieves superior composition
performance qualitatively and quantitatively.
