  This study considers the 3D human pose estimation problem in a single RGB
image by proposing a conditional random field (CRF) model over 2D poses, in
which the 3D pose is obtained as a byproduct of the inference process. The
unary term of the proposed CRF model is defined based on a powerful heat-map
regression network, which has been proposed for 2D human pose estimation. This
study also presents a regression network for lifting the 2D pose to 3D pose and
proposes the prior term based on the consistency between the estimated 3D pose
and the 2D pose. To obtain the approximate solution of the proposed CRF model,
the N-best strategy is adopted. The proposed inference algorithm can be viewed
as sequential processes of bottom-up generation of 2D and 3D pose proposals
from the input 2D image based on deep networks and top-down verification of
such proposals by checking their consistencies. To evaluate the proposed
method, we use two large-scale datasets: Human3.6M and HumanEva. Experimental
results show that the proposed method achieves the state-of-the-art 3D human
pose estimation performance.
