  We propose a new task towards more practical application for image generation
- high-quality image synthesis from salient object layout. This new setting
allows users to provide the layout of salient objects only (i.e., foreground
bounding boxes and categories), and lets the model complete the drawing with an
invented background and a matching foreground. Two main challenges spring from
this new task: (i) how to generate fine-grained details and realistic textures
without segmentation map input; and (ii) how to create a background and weave
it seamlessly into standalone objects. To tackle this, we propose Background
Hallucination Generative Adversarial Network (BachGAN), which first selects a
set of segmentation maps from a large candidate pool via a background retrieval
module, then encodes these candidate layouts via a background fusion module to
hallucinate a suitable background for the given objects. By generating the
hallucinated background representation dynamically, our model can synthesize
high-resolution images with both photo-realistic foreground and integral
background. Experiments on Cityscapes and ADE20K datasets demonstrate the
advantage of BachGAN over existing methods, measured on both visual fidelity of
generated images and visual alignment between output images and input layouts.
