  We present a generative model of images that explicitly reasons over the set
of objects they show. Our model learns a structured latent representation that
separates objects from each other and from the background; unlike prior works,
it explicitly represents the 2D position and depth of each object, as well as
an embedding of its segmentation mask and appearance. The model can be trained
from images alone in a purely unsupervised fashion without the need for object
masks or depth information. Moreover, it always generates complete objects,
even though a significant fraction of training images contain occlusions.
Finally, we show that our model can infer decompositions of novel images into
their constituent objects, including accurate prediction of depth ordering and
segmentation of occluded parts.
