3D Human Pose Estimation Using Convolutional Neural Networks with 2D Pose Information
This addresses the problem of 3D human pose estimation for computer vision applications, but it is incremental as it builds on existing CNN approaches.
The paper tackles 3D human pose estimation from images by using CNNs with 2D pose information and relative joint positions, achieving comparable performance to state-of-the-art methods on the Human 3.6m dataset.
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.