665 citations · 776 across the 4 of their papers we have counts for
7 papers
Bottom-Up Human Pose Estimation Via Disentangled Keypoint Regression
Zigang Geng, Ke Sun, Bin Xiao +2
In this paper, we are interested in the bottom-up paradigm of estimating human poses from an image. We study the dense keypoint regression framework that is previously inferior to…
Bottom-Up Human Pose Estimation by Ranking Heatmap-Guided Adaptive Keypoint Estimates
Ke Sun, Zigang Geng, Depu Meng +4
The typical bottom-up human pose estimation framework includes two stages, keypoint detection and grouping. Most existing works focus on developing grouping algorithms, e.g., assoc…
HigherHRNet: Scale-Aware Representation Learning for Bottom-Up Human Pose Estimation
Bowen Cheng, Bin Xiao, Jingdong Wang +3
Bottom-up human pose estimation methods have difficulties in predicting the correct pose for small persons due to challenges in scale variation. In this paper, we present HigherHRN…
Deep High-Resolution Representation Learning for Visual Recognition
Jingdong Wang, Ke Sun, Tianheng Cheng +9
High-resolution representations are essential for position-sensitive vision problems, such as human pose estimation, semantic segmentation, and object detection. Existing state-of-…
High-Resolution Representations for Labeling Pixels and Regions
Ke Sun, Yang Zhao, Borui Jiang +7
High-resolution representation learning plays an essential role in many vision problems, e.g., pose estimation and semantic segmentation. The high-resolution network (HRNet)~\cite{…
Deep High-Resolution Representation Learning for Human Pose Estimation
Ke Sun, Bin Xiao, Dong Liu +1
This is an official pytorch implementation of Deep High-Resolution Representation Learning for Human Pose Estimation. In this work, we are interested in the human pose estimation p…