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We propose a method to learn 3D deformable object categories from raw single-view images, without external supervision. The method is based on an autoencoder that factors each input image into depth, albedo, viewpoint and illumination. In order to disentangle these components without supervision, we use the fact that many object categories have, at least approximately, a symmetric structure. We show that reasoning about illumination allows us to exploit the underlying object symmetry even ifdoi:10.1109/tpami.2021.3076536 pmid:33914682 fatcat:iggro2bzcjfxra5zovuzjui2ce