  The goal of many computer vision systems is to transform image pixels into 3D
representations. Recent popular models use neural networks to regress directly
from pixels to 3D object parameters. Such an approach works well when
supervision is available, but in problems like human pose and shape estimation,
it is difficult to obtain natural images with 3D ground truth. To go one step
further, we propose a new architecture that facilitates unsupervised, or
lightly supervised, learning. The idea is to break the problem into a series of
transformations between increasingly abstract representations. Each step
involves a cycle designed to be learnable without annotated training data, and
the chain of cycles delivers the final solution. Specifically, we use 2D body
part segments as an intermediate representation that contains enough
information to be lifted to 3D, and at the same time is simple enough to be
learned in an unsupervised way. We demonstrate the method by learning 3D human
pose and shape from un-paired and un-annotated images. We also explore varying
amounts of paired data and show that cycling greatly alleviates the need for
paired data. While we present results for modeling humans, our formulation is
general and can be applied to other vision problems.
