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.

本文提出了一种使用卷积神经网络进行端到端学习的 3D 人体姿势估计方法，通过 CNN 学习找出相对于其他关节的相对 3D 位置，并通过将 2D 姿势信息与图像特征连接以及通过关于多个关节的相对位置信息的组合来获得更准确的 3D 姿势。实验结果表明，该方法在 Human 3.6m 数据集上实现了与最先进方法可比较的性能。