2D Human Pose Estimation
63 papers with code • 5 benchmarks • 22 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.
Benchmarks
These leaderboards are used to track progress in 2D Human Pose Estimation
| Trend | Dataset | Best Model | Paper | Code | Compare |
|---|---|---|---|---|---|
|
|||||
|
|||||
|
|||||
|
|||||
|
Libraries
Use these libraries to find 2D Human Pose Estimation models and implementationsDatasets
JHMDB
AGORA
OCHuman
UAV-Human
COCO-WholeBody
Alibaba Cluster Trace
DeepSport Dataset
Most implemented papers
Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields
ZheC/Realtime_Multi-Person_Pose_Estimation
•
•
CVPR 2017
We present an approach to efficiently detect the 2D pose of multiple people in an image.
Deep High-Resolution Representation Learning for Human Pose Estimation
leoxiaobin/deep-high-resolution-net.pytorch
•
•
CVPR 2019
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.
Simple Baselines for Human Pose Estimation and Tracking
leoxiaobin/pose.pytorch
•
•
ECCV 2018
There has been significant progress on pose estimation and increasing interests on pose tracking in recent years.
HigherHRNet: Scale-Aware Representation Learning for Bottom-Up Human Pose Estimation
HRNet/Higher-HRNet-Human-Pose-Estimation
•
•
CVPR 2020
HigherHRNet even surpasses all top-down methods on CrowdPose test (67. 6% AP), suggesting its robustness in crowded scene.
RMPE: Regional Multi-person Pose Estimation
MVIG-SJTU/AlphaPose
•
•
ICCV 2017
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.
Simple Pose: Rethinking and Improving a Bottom-up Approach for Multi-Person Pose Estimation
hellojialee/Improved-Body-Parts
•
•
We rethink a well-know bottom-up approach for multi-person pose estimation and propose an improved one.
Near-Optimal Representation Learning for Hierarchical Reinforcement Learning
tensorflow/models
•
•
ICLR 2019
We study the problem of representation learning in goal-conditioned hierarchical reinforcement learning.
BlazePose: On-device Real-time Body Pose tracking
google/mediapipe
•
•
We present BlazePose, a lightweight convolutional neural network architecture for human pose estimation that is tailored for real-time inference on mobile devices.
Pose2Seg: Detection Free Human Instance Segmentation
liruilong940607/Pose2Seg
•
•
CVPR 2019
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.
Associative Embedding: End-to-End Learning for Joint Detection and Grouping
open-mmlab/mmpose
•
•
NeurIPS 2017
We introduce associative embedding, a novel method for supervising convolutional neural networks for the task of detection and grouping.
