3D Human Pose Estimation Using Cascade of Multiple Neural Networks

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Authors Van-Thanh Hoang, Kang-Hyun Jo
Journal/Conference Name IEEE Transactions on Industrial Informatics
Paper Category
Paper Abstract Estimating three-dimensional (3-D) human poses from a given two-dimensional (2-D) shape is still an inherently ill-posed problem in computer vision. This paper proposes a method called cascade of multiple neural networks (CMNN) to solve this problem in following two steps 1) create the initial estimated 3-D shape using the Zhou et al. method with a small number of basis shapes and 2) make this initial shape more alike to the original shape by using the CMNN. In comparing to existing works, the proposed method shows a significant outperformance in both accuracy and processing time. This paper also introduces a new system called Human3D that can estimate the 3-D pose of all people in a single RGB image. This system comprises two part convolution pose machine (CPM) for estimating 2-D poses of all people in an RGB image and CMNN for reconstructing 3-D poses of them from outputs of the CPM.
Date of publication 2018
Code Programming Language Matlab
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