  Following the successful application of deep convolutional neural networks to
2d human pose estimation, the next logical problem to solve is 3d human pose
estimation from monocular images. While previous solutions have shown some
success, they do not fully utilize the depth information from the 2d inputs.
With the goal of addressing this depth ambiguity, we build a system that takes
2d joint locations as input along with their estimated depth value and predicts
their 3d positions in camera coordinates. Given the inherent noise and
inaccuracy from estimating depth maps from monocular images, we perform an
extensive statistical analysis showing that given this noise there is still a
statistically significant correlation between the predicted depth values and
the third coordinate of camera coordinates. We further explain how the
state-of-the-art results we achieve on the H3.6M validation set are due to the
additional input of depth. Notably, our results are produced on neural network
that accepts a low dimensional input and be integrated into a real-time system.
Furthermore, our system can be combined with an off-the-shelf 2d pose detector
and a depth map predictor to perform 3d pose estimation in the wild.
