  We present an algorithm to generate diverse foreground objects and composite
them into background images using a GAN architecture. Given an object class, a
user-provided bounding box, and a background image, we first use a mask
generator to create an object shape, and then use a texture generator to fill
the mask such that the texture integrates with the background. By separating
the problem of object insertion into these two stages, we show that our model
allows us to improve the realism of diverse object generation that also agrees
with the provided background image. Our results on the challenging COCO dataset
show improved overall quality and diversity compared to state-of-the-art object
insertion approaches.
