  Generative Adversarial Networks (GANs) have become the de-facto standard in
image synthesis. However, without considering the foreground-background
decomposition, existing GANs tend to capture excessive content correlation
between foreground and background, thus constraining the diversity in image
generation. This paper presents a novel Foreground-Background Composition GAN
(FBC-GAN) that performs image generation by generating foreground objects and
background scenes concurrently and independently, followed by composing them
with style and geometrical consistency. With this explicit design, FBC-GAN can
generate images with foregrounds and backgrounds that are mutually independent
in contents, thus lifting the undesirably learned content correlation
constraint and achieving superior diversity. It also provides excellent
flexibility by allowing the same foreground object with different background
scenes, the same background scene with varying foreground objects, or the same
foreground object and background scene with different object positions, sizes
and poses. It can compose foreground objects and background scenes sampled from
different datasets as well. Extensive experiments over multiple datasets show
that FBC-GAN achieves competitive visual realism and superior diversity as
compared with state-of-the-art methods.
