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.

Greatest papers with code

Deep High-Resolution Representation Learning for Human Pose Estimation

PaddlePaddle/PaddleDetection 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.

Instance Segmentation Multi-Person Pose Estimation +3

Simple Baselines for Human Pose Estimation and Tracking

PaddlePaddle/PaddleDetection ECCV 2018

There has been significant progress on pose estimation and increasing interests on pose tracking in recent years.

Pose Tracking

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.

Human Detection Multi-Person Pose Estimation

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

osmr/imgclsmob 24 Nov 2019

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

Multi-Person Pose Estimation

Whole-Body Human Pose Estimation in the Wild

open-mmlab/mmpose ECCV 2020

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.

Facial Landmark Detection Hand Pose Estimation +1

Pose2Seg: Detection Free Human Instance Segmentation

open-mmlab/mmpose 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.

Human Instance Segmentation Object Detection +1

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.

Instance Segmentation Multi-Person Pose Estimation