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We present an unsupervised learning framework for decomposing images intolayers of automatically discovered object models. Contrary to recent approachesthat model image layers with autoencoder networks, we represent them asexplicit transformations of a small set of prototypical images. Our model hasthree main components: (i) a set of object prototypes in the form of learnableimages with a transparency channel, which we refer to as sprites; (ii)differentiable parametric functions predicting occlusions and transformationparameters necessary to instantiate the sprites in a given image; (iii) alayered image formation model with occlusion for compositing these instancesinto complete images including background. By jointly learning the sprites andocclusion/transformation predictors to reconstruct images, our approach notonly yields accurate layered image decompositions, but also identifies objectcategories and instance parameters. We first validate our approach by providingresults on par with the state of the art on standard multi-object syntheticbenchmarks (Tetrominoes, Multi-dSprites, CLEVR6). We then demonstrate theapplicability of our model to real images in tasks that include clustering(SVHN, GTSRB), cosegmentation (Weizmann Horse) and object discovery fromunfiltered social network images. To the best of our knowledge, our approach isthe first layered image decomposition algorithm that learns an explicit andshared concept of object type, and is robust enough to be applied to realimages.

Unsupervised Layered Image Decomposition into Object Prototypes