Realtime Multi-person 2D Pose Estimation Using Part Affinity Fields

Published in

Computer Vision and Pattern Recognition(2016)

External Links:

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TL;DR

This work presents an approach to efficiently detect the 2D pose of multiple people in an image using a nonparametric representation, which it refers to as Part Affinity Fields (PAFs), to learn to associate body parts with individuals in the image.

Abstract

We present an approach to efficiently detect the 2D pose of multiple people in an image. The approach uses a nonparametric representation, which we refer to as Part Affinity Fields (PAFs), to learn to associate body parts with individuals in the image. The architecture encodes global context, allowing a greedy bottom-up parsing step that maintains high accuracy while achieving realtime performance, irrespective of the number of people in the image. The architecture is designed to jointly learn part locations and their association via two branches of the same sequential prediction process. Our method placed first in the inaugural COCO 2016 keypoints challenge, and significantly exceeds the previous state-of-the-art result on the MPII Multi-Person benchmark, both in performance and efficiency.

Authors

T. Simon

6 papers

Yaser Sheikh

8 papers

Zhe Cao

4 papers

References43 items

Towards Accurate Multi-person Pose Estimation in the Wild

Human Pose Estimation via Convolutional Part Heatmap Regression

Multi-person Pose Estimation with Local Joint-to-Person Associations

Recurrent Human Pose Estimation

DeeperCut: A Deeper, Stronger, and Faster Multi-person Pose Estimation Model

Realtime Multi-person 2D Pose Estimation Using Part Affinity Fields

Published in

Computer Vision and Pattern Recognition(2016)

External Links:

Generate Graph DownloadPDF

TL;DR

Abstract

Authors

T. Simon

6 papers

Yaser Sheikh

8 papers

Zhe Cao

4 papers

References43 items

Towards Accurate Multi-person Pose Estimation in the Wild

Human Pose Estimation via Convolutional Part Heatmap Regression

Multi-person Pose Estimation with Local Joint-to-Person Associations

Recurrent Human Pose Estimation

DeeperCut: A Deeper, Stronger, and Faster Multi-person Pose Estimation Model

Stacked Hourglass Networks for Human Pose Estimation

Convolutional Pose Machines

Deep Residual Learning for Image Recognition

SSD: Single Shot MultiBox Detector

DeepCut: Joint Subset Partition and Labeling for Multi Person Pose Estimation

Flowing ConvNets for Human Pose Estimation in Videos

Efficient object localization using Convolutional Networks

Pose Machines: Articulated Pose Estimation via Inference Machines

Very Deep Convolutional Networks for Large-Scale Image Recognition

Articulated Pose Estimation by a Graphical Model with Image Dependent Pairwise Relations

Using k-Poselets for Detecting People and Localizing Their Keypoints

Joint Training of a Convolutional Network and a Graphical Model for Human Pose Estimation

Multi-source Deep Learning for Human Pose Estimation

2D Human Pose Estimation: New Benchmark and State of the Art Analysis

Microsoft COCO: Common Objects in Context

DeepPose: Human Pose Estimation via Deep Neural Networks

Articulated Human Detection with Flexible Mixtures of Parts

Poselet Conditioned Pictorial Structures

Human Pose Estimation Using Body Parts Dependent Joint Regressors

Using Linking Features in Learning Non-parametric Part Models

Articulated people detection and pose estimation: Reshaping the future

Articulated part-based model for joint object detection and pose estimation

Articulated pose estimation with flexible mixtures-of-parts

We Are Family: Joint Pose Estimation of Multiple Persons

Monocular 3D pose estimation and tracking by detection

Understanding the difficulty of training deep feedforward neural networks

Pictorial structures revisited: People detection and articulated pose estimation

Multiple Tree Models for Occlusion and Spatial Constraints in Human Pose Estimation

Measure Locally, Reason Globally: Occlusion-sensitive Articulated Pose Estimation

Beyond trees: common-factor models for 2D human pose recovery

Strike a pose: tracking people by finding stylized poses

Learning long-term dependencies with gradient descent is difficult

Introduction to graph theory

The Hungarian method for the assignment problem

Clustered Pose and Nonlinear Appearance Models for Human Pose Estimation

Pictorial Structures for Object Recognition

Gradient Flow in Recurrent Nets: the Difficulty of Learning Long-Term Dependencies

Mscoco keypoint evaluation metric

Field of Study

Computer Science

Journal Information

Name

2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

Page

1800-1807

Venue Information

Name

Computer Vision and Pattern Recognition

Type

conference

URL

https://ieeexplore.ieee.org/xpl/conhome.jsp?punumber=1000147

Alternate Names

CVPR
Comput Vis Pattern Recognit