3-D Human Pose Estimation Using Cascade of Multiple Neural Networks

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 esti...

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Bibliographic Details
Published in:IEEE transactions on industrial informatics 2019-04, Vol.15 (4), p.2064-2072
Main Authors: Hoang, Van-Thanh, Jo, Kang-Hyun
Format: Article
Language:English
Subjects:
3-D human pose estimation
Cameras
Cascade of neural networks
Computer vision
Convolution
Dictionaries
Estimation
human three-dimensional (3-D) system
Ill posed problems
Image reconstruction
neural network
Neural networks
Pose estimation
Shape
Three-dimensional displays
Two dimensional displays
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Summary: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.
ISSN:1551-3203
1941-0050
DOI:10.1109/TII.2018.2864824