  We propose a new self-supervised method for predicting 3D human body pose
from a single image. The prediction network is trained from a dataset of
unlabelled images depicting people in typical poses and a set of unpaired 2D
poses. By minimising the need for annotated data, the method has the potential
for rapid application to pose estimation of other articulated structures (e.g.
animals). The self-supervision comes from an earlier idea exploiting
consistency between predicted pose under 3D rotation. Our method is a
substantial advance on state-of-the-art self-supervised methods in training a
mapping directly from images, without limb articulation constraints or any 3D
empirical pose prior. We compare performance with state-of-the-art
self-supervised methods using benchmark datasets that provide images and
ground-truth 3D pose (Human3.6M, MPI-INF-3DHP). Despite the reduced requirement
for annotated data, we show that the method outperforms on Human3.6M and
matches performance on MPI-INF-3DHP. Qualitative results on a dataset of human
hands show the potential for rapidly learning to predict 3D pose for
articulated structures other than the human body.
