Depth-images-based pose estimation using regression forests and graphical models

By Li He, Guijin Wang, Qingmin Liao, Jing-Hao Xue. 2015

27 citations recorded by the source corpus.

Subfields: Aerospace Engineering, Computer Vision and Pattern Recognition

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Aerospace Engineering

1. Alexandre Alahi
2. Zelun Luo
3. Serena Yeung
4. Albert Haque
5. Li Fei-Fei
6. Silvio Savarese
7. Subhransu Maji
8. Feng Zhou

In Computer Vision and Pattern Recognition

1. Jing-Hao Xue
2. Li Fei-Fei
3. Luc Van Gool
4. Ming-Hsuan Yang
5. Alexandre Alahi
6. Trevor Darrell
7. Xiaogang Wang
8. Dacheng Tao

Citations in the corpus

• A novel hierarchical framework for human action recognition (2016, background): “In fact 3D human skeleton, which could be reliably estimated from depth maps in real time [23–25], is an efficient and concise surrogate to describe the human poses.”
• Towards Viewpoint Invariant 3D Human Pose Estimation (2016, methodology): “To further constrain the output space similar to RGB methods, graphical models [29,20] impose kinematic constraints to improve full-body pose estimation.”