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We propose the Canonical 3D Deformer Map, a new representation of the 3Dshape of common object categories that can be learned from a collection of 2Dimages of independent objects. Our method builds in a novel way on conceptsfrom parametric deformation models, non-parametric 3D reconstruction, andcanonical embeddings, combining their individual advantages. In particular, itlearns to associate each image pixel with a deformation model of thecorresponding 3D object point which is canonical, i.e. intrinsic to theidentity of the point and shared across objects of the category. The result isa method that, given only sparse 2D supervision at training time, can, at testtime, reconstruct the 3D shape and texture of objects from single views, whileestablishing meaningful dense correspondences between object instances. It alsoachieves state-of-the-art results in dense 3D reconstruction on publicin-the-wild datasets of faces, cars, and birds.

Canonical 3D Deformer Maps: Unifying parametric and non-parametric methods for dense weakly-supervised category reconstruction