3 research outputs found
Canonical 3D Deformer Maps: Unifying parametric and non-parametric methods for dense weakly-supervised category reconstruction
We propose the Canonical 3D Deformer Map, a new representation of the 3D
shape of common object categories that can be learned from a collection of 2D
images of independent objects. Our method builds in a novel way on concepts
from parametric deformation models, non-parametric 3D reconstruction, and
canonical embeddings, combining their individual advantages. In particular, it
learns to associate each image pixel with a deformation model of the
corresponding 3D object point which is canonical, i.e. intrinsic to the
identity of the point and shared across objects of the category. The result is
a method that, given only sparse 2D supervision at training time, can, at test
time, reconstruct the 3D shape and texture of objects from single views, while
establishing meaningful dense correspondences between object instances. It also
achieves state-of-the-art results in dense 3D reconstruction on public
in-the-wild datasets of faces, cars, and birds.Comment: Published at NeurIPS 202