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Sensors 2016, 16(2), 263;

A Layered Approach for Robust Spatial Virtual Human Pose Reconstruction Using a Still Image

State Key Lab of Virtual Reality Technology and Systems, Beihang university, Xueyuan Road No.37, Haidian District, Beijing 100000, China
Author to whom correspondence should be addressed.
Academic Editor: Vittorio M. N. Passaro
Received: 30 December 2015 / Revised: 4 February 2016 / Accepted: 16 February 2016 / Published: 20 February 2016
(This article belongs to the Special Issue Sensors for Robots)
PDF [1356 KB, uploaded 20 February 2016]


Pedestrian detection and human pose estimation are instructive for reconstructing a three-dimensional scenario and for robot navigation, particularly when large amounts of vision data are captured using various data-recording techniques. Using an unrestricted capture scheme, which produces occlusions or breezing, the information describing each part of a human body and the relationship between each part or even different pedestrians must be present in a still image. Using this framework, a multi-layered, spatial, virtual, human pose reconstruction framework is presented in this study to recover any deficient information in planar images. In this framework, a hierarchical parts-based deep model is used to detect body parts by using the available restricted information in a still image and is then combined with spatial Markov random fields to re-estimate the accurate joint positions in the deep network. Then, the planar estimation results are mapped onto a virtual three-dimensional space using multiple constraints to recover any deficient spatial information. The proposed approach can be viewed as a general pre-processing method to guide the generation of continuous, three-dimensional motion data. The experiment results of this study are used to describe the effectiveness and usability of the proposed approach. View Full-Text
Keywords: body part detection; pose estimation; spatial pose reconstruction; deep model body part detection; pose estimation; spatial pose reconstruction; deep model

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Guo, C.; Ruan, S.; Liang, X.; Zhao, Q. A Layered Approach for Robust Spatial Virtual Human Pose Reconstruction Using a Still Image. Sensors 2016, 16, 263.

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