  Image composition aims to blend multiple objects to form a harmonized image.
Existing approaches often assume precisely segmented and intact objects. Such
assumptions, however, are hard to satisfy in unconstrained scenarios. We
present Amodal Instance Composition for compositing imperfect -- potentially
incomplete and/or coarsely segmented -- objects onto a target image. We first
develop object shape prediction and content completion modules to synthesize
the amodal contents. We then propose a neural composition model to blend the
objects seamlessly. Our primary technical novelty lies in using separate
foreground/background representations and blending mask prediction to alleviate
segmentation errors. Our results show state-of-the-art performance on public
COCOA and KINS benchmarks and attain favorable visual results across diverse
scenes. We demonstrate various image composition applications such as object
insertion and de-occlusion.
