While there has been a success in 2D human pose estimation with convolutional
neural networks (CNNs), 3D human pose estimation has not been thoroughly
studied. In this paper, we tackle the 3D human pose estimation task with
end-to-end learning using CNNs...
Relative 3D positions between one joint and the
other joints are learned via CNNs. The proposed method improves the performance
of CNN with two novel ideas. First, we added 2D pose information to estimate a
3D pose from an image by concatenating 2D pose estimation result with the
features from an image. Second, we have found that more accurate 3D poses are
obtained by combining information on relative positions with respect to
multiple joints, instead of just one root joint. Experimental results show that
the proposed method achieves comparable performance to the state-of-the-art
methods on Human 3.6m dataset.