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We present a generative model of images that explicitly reasons over the setof objects they show. Our model learns a structured latent representation thatseparates objects from each other and from the background; unlike prior works,it explicitly represents the 2D position and depth of each object, as well asan embedding of its segmentation mask and appearance. The model can be trainedfrom images alone in a purely unsupervised fashion without the need for objectmasks 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 intotheir constituent objects, including accurate prediction of depth ordering andsegmentation of occluded parts.

Object-Centric Image Generation with Factored Depths, Locations, and Appearances