2D Human Pose Estimation

47 papers with code • 5 benchmarks • 17 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.

Libraries

Use these libraries to find 2D Human Pose Estimation models and implementations

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.

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.

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.

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.

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 17 Jun 2020

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.

Simple Pose: Rethinking and Improving a Bottom-up Approach for Multi-Person Pose Estimation

hellojialee/Improved-Body-Parts 24 Nov 2019

We rethink a well-know bottom-up approach for multi-person pose estimation and propose an improved one.