  This paper shows that it is possible to learn models for monocular 3D
reconstruction of articulated objects (e.g., horses, cows, sheep), using as few
as 50-150 images labeled with 2D keypoints. Our proposed approach involves
training category-specific keypoint estimators, generating 2D keypoint
pseudo-labels on unlabeled web images, and using both the labeled and
self-labeled sets to train 3D reconstruction models. It is based on two key
insights: (1) 2D keypoint estimation networks trained on as few as 50-150
images of a given object category generalize well and generate reliable
pseudo-labels; (2) a data selection mechanism can automatically create a
"curated" subset of the unlabeled web images that can be used for training --
we evaluate four data selection methods. Coupling these two insights enables us
to train models that effectively utilize web images, resulting in improved 3D
reconstruction performance for several articulated object categories beyond the
fully-supervised baseline. Our approach can quickly bootstrap a model and
requires only a few images labeled with 2D keypoints. This requirement can be
easily satisfied for any new object category. To showcase the practicality of
our approach for predicting the 3D shape of arbitrary object categories, we
annotate 2D keypoints on giraffe and bear images from COCO -- the annotation
process takes less than 1 minute per image.
