3D human pose and shape estimation with dense correspondence from a single depth image

Author(s):  
Kangkan Wang ◽  
Guofeng Zhang ◽  
Jian Yang
2014 ◽  
Vol 41 (2) ◽  
pp. 473-486 ◽  
Author(s):  
Dong-Luong Dinh ◽  
Myeong-Jun Lim ◽  
Nguyen Duc Thang ◽  
Sungyoung Lee ◽  
Tae-Seong Kim

2021 ◽  
Author(s):  
Jiefeng Li ◽  
Chao Xu ◽  
Zhicun Chen ◽  
Siyuan Bian ◽  
Lixin Yang ◽  
...  

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-12 ◽  
Author(s):  
Haikuan Wang ◽  
Feixiang Zhou ◽  
Wenju Zhou ◽  
Ling Chen

The recognition of human pose based on machine vision usually results in a low recognition rate, low robustness, and low operating efficiency. That is mainly caused by the complexity of the background, as well as the diversity of human pose, occlusion, and self-occlusion. To solve this problem, a feature extraction method combining directional gradient of depth feature (DGoD) and local difference of depth feature (LDoD) is proposed in this paper, which uses a novel strategy that incorporates eight neighborhood points around a pixel for mutual comparison to calculate the difference between the pixels. A new data set is then established to train the random forest classifier, and a random forest two-way voting mechanism is adopted to classify the pixels on different parts of the human body depth image. Finally, the gravity center of each part is calculated and a reasonable point is selected as the joint to extract human skeleton. The experimental results show that the robustness and accuracy are significantly improved, associated with a competitive operating efficiency by evaluating our approach with the proposed data set.


2018 ◽  
Vol 12 (6) ◽  
pp. 919-924 ◽  
Author(s):  
Qingqiang Wu ◽  
Guanghua Xu ◽  
Min Li ◽  
Longting Chen ◽  
Xin Zhang ◽  
...  

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