human pose
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Sensors ◽  
2022 ◽  
Vol 22 (2) ◽  
pp. 632
Author(s):  
Jie Li ◽  
Zhixing Wang ◽  
Bo Qi ◽  
Jianlin Zhang ◽  
Hu Yang

In this paper, a mutually enhanced modeling method (MEMe) is presented for human pose estimation, which focuses on enhancing lightweight model performance, but with low complexity. To obtain higher accuracy, a traditional model scale is largely expanded with heavy deployment difficulties. However, for a more lightweight model, there is a large performance gap compared to the former; thus, an urgent need for a way to fill it. Therefore, we propose a MEMe to reconstruct a lightweight baseline model, EffBase transferred intuitively from EfficientDet, into the efficient and effective pose (EEffPose) net, which contains three mutually enhanced modules: the Enhanced EffNet (EEffNet) backbone, the total fusion neck (TFNeck), and the final attention head (FAHead). Extensive experiments on COCO and MPII benchmarks show that our MEMe-based models reach state-of-the-art performances, with limited parameters. Specifically, in the same conditions, our EEffPose-P0 with 256 × 192 can use only 8.98 M parameters to achieve 75.4 AP on the COCO val set, which outperforms HRNet-W48, but with only 14% of its parameters.


Author(s):  
Xue Wang ◽  
Runyang Feng ◽  
Haoming Chen ◽  
Roger Zimmermann ◽  
Zhenguang Liu ◽  
...  

Author(s):  
Jielu Yan ◽  
MingLiang Zhou ◽  
Jinli Pan ◽  
Meng Yin ◽  
Bin Fang

3D human pose estimation describes estimating 3D articulation structure of a person from an image or a video. The technology has massive potential because it can enable tracking people and analyzing motion in real time. Recently, much research has been conducted to optimize human pose estimation, but few works have focused on reviewing 3D human pose estimation. In this paper, we offer a comprehensive survey of the state-of-the-art methods for 3D human pose estimation, referred to as pose estimation solutions, implementations on images or videos that contain different numbers of people and advanced 3D human pose estimation techniques. Furthermore, different kinds of algorithms are further subdivided into sub-categories and compared in light of different methodologies. To the best of our knowledge, this is the first such comprehensive survey of the recent progress of 3D human pose estimation and will hopefully facilitate the completion, refinement and applications of 3D human pose estimation.


Author(s):  
Manuel J. Marín-Jiménez ◽  
Javier Romero ◽  
Hao Li ◽  
Grégory Rogez
Keyword(s):  

2022 ◽  
pp. 1-1
Author(s):  
Huan Liu ◽  
Wentao Liu ◽  
Zhixiang Chi ◽  
Yang Wang ◽  
Yuanhao Yu ◽  
...  

2022 ◽  
pp. 1-1
Author(s):  
Wenhao Li ◽  
Hong Liu ◽  
Runwei Ding ◽  
Mengyuan Liu ◽  
Pichao Wang ◽  
...  

2022 ◽  
Author(s):  
Alberto Lamas ◽  
Siham Tabik ◽  
Antonio Cano Montes ◽  
Francisco Pérez-Hernández ◽  
Jorge García ◽  
...  

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