Keypoint Detection

109 papers with code • 7 benchmarks • 8 datasets

Keypoint detection involves simultaneously detecting people and localizing their keypoints. Keypoints are the same thing as interest points. They are spatial locations, or points in the image that define what is interesting or what stand out in the image. They are invariant to image rotation, shrinkage, translation, distortion, and so on.

( Image credit: PifPaf: Composite Fields for Human Pose Estimation; "Learning to surf" by fotologic, license: CC-BY-2.0 )

Libraries

Use these libraries to find Keypoint Detection models and implementations
11 papers
2,311
6 papers
2,630
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Most implemented papers

Mask R-CNN

matterport/Mask_RCNN ICCV 2017

Our approach efficiently detects objects in an image while simultaneously generating a high-quality segmentation mask for each instance.

Objects as Points

xingyizhou/CenterNet 16 Apr 2019

We model an object as a single point --- the center point of its bounding box.

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.

OpenPose: Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields

CMU-Perceptual-Computing-Lab/openpose 18 Dec 2018

OpenPose: Real-time multi-person keypoint detection library for body, face, hands, and foot estimation

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.

Non-local Neural Networks

facebookresearch/video-nonlocal-net CVPR 2018

Both convolutional and recurrent operations are building blocks that process one local neighborhood at a time.

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.

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

DeepLabCut/DeepLabCut 10 May 2016

The goal of this paper is to advance the state-of-the-art of articulated pose estimation in scenes with multiple people.

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.