2D Human Pose Estimation
33 papers with code • 3 benchmarks • 8 datasets
What is Human Pose Estimation? Human pose estimation is the process of estimating the configuration of the body (pose) from a single, typically monocular, image. Background. Human pose estimation is one of the key problems in computer vision that has been studied for well over 15 years. The reason for its importance is the abundance of applications that can benefit from such a technology. For example, human pose estimation allows for higher-level reasoning in the context of human-computer interaction and activity recognition; it is also one of the basic building blocks for marker-less motion capture (MoCap) technology. MoCap technology is useful for applications ranging from character animation to clinical analysis of gait pathologies.
HigherHRNet even surpasses all top-down methods on CrowdPose test (67. 6% AP), suggesting its robustness in crowded scene.
Ranked #2 on Pose Estimation on UAV-Human
We start from a high-resolution subnetwork as the first stage, gradually add high-to-low resolution subnetworks one by one to form more stages, and connect the mutli-resolution subnetworks in parallel.
Ranked #1 on Keypoint Detection on COCO test-dev
There has been significant progress on pose estimation and increasing interests on pose tracking in recent years.
Ranked #2 on Keypoint Detection on COCO test-challenge
In this paper, we propose a novel regional multi-person pose estimation (RMPE) framework to facilitate pose estimation in the presence of inaccurate human bounding boxes.
Ranked #1 on Keypoint Detection on MPII Multi-Person
We rethink a well-know bottom-up approach for multi-person pose estimation and propose an improved one.
Ranked #8 on Multi-Person Pose Estimation on COCO test-dev
This paper investigates the task of 2D human whole-body pose estimation, which aims to localize dense landmarks on the entire human body including face, hands, body, and feet.
Ranked #2 on 2D Human Pose Estimation on COCO-WholeBody
We demonstrate that our pose-based framework can achieve better accuracy than the state-of-art detection-based approach on the human instance segmentation problem, and can moreover better handle occlusion.
Ranked #1 on Human Instance Segmentation on OCHuman
We introduce associative embedding, a novel method for supervising convolutional neural networks for the task of detection and grouping.
Ranked #5 on Keypoint Detection on MPII Multi-Person