  We propose a method SPGNet for 3D human pose estimation that mixes
multi-dimensional re-projection into supervised learning. In this method, the
2D-to-3D-lifting network predicts the global position and coordinates of the 3D
human pose. Then, we re-project the estimated 3D pose back to the 2D key points
along with spatial adjustments. The loss functions compare the estimated 3D
pose with the 3D pose ground truth, and re-projected 2D pose with the input 2D
pose. In addition, we propose a kinematic constraint to restrict the predicted
target with constant human bone length. Based on the estimation results for the
dataset Human3.6M, our approach outperforms many state-of-the-art methods both
qualitatively and quantitatively.
