  Conventional 3D human pose estimation relies on first detecting 2D body
keypoints and then solving the 2D to 3D correspondence problem.Despite the
promising results, this learning paradigm is highly dependent on the quality of
the 2D keypoint detector, which is inevitably fragile to occlusions and
out-of-image absences.In this paper,we propose a novel Pose Orientation Net
(PONet) that is able to robustly estimate 3D pose by learning orientations
only, hence bypassing the error-prone keypoint detector in the absence of image
evidence. For images with partially invisible limbs, PONet estimates the 3D
orientation of these limbs by taking advantage of the local image evidence to
recover the 3D pose.Moreover, PONet is competent to infer full 3D poses even
from images with completely invisible limbs, by exploiting the orientation
correlation between visible limbs to complement the estimated poses,further
improving the robustness of 3D pose estimation.We evaluate our method on
multiple datasets, including Human3.6M, MPII, MPI-INF-3DHP, and 3DPW. Our
method achieves results on par with state-of-the-art techniques in ideal
settings, yet significantly eliminates the dependency on keypoint detectors and
the corresponding computation burden. In highly challenging scenarios, such as
truncation and erasing, our method performs very robustly and yields much
superior results as compared to state of the art,demonstrating its potential
for real-world applications.
