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。

使用带有2D姿态信息的卷积神经网络进行3D人体姿势估计