  Estimating three-dimensional human poses from the positions of
two-dimensional joints has shown promising results.However, using
two-dimensional joint coordinates as input loses more information than
image-based approaches and results in ambiguity.In order to overcome this
problem, we combine bone length and camera parameters with two-dimensional
joint coordinates for input.This combination is more discriminative than the
two-dimensional joint coordinates in that it can improve the accuracy of the
model's prediction depth and alleviate the ambiguity that comes from projecting
three-dimensional coordinates into two-dimensional space. Furthermore, we
introduce direction constraints which can better measure the difference between
the ground truth and the output of the proposed model. The experimental results
on the H36M show that the method performed better than other state-of-the-art
three-dimensional human pose estimation approaches.
