Pose Estimation
1341 papers with code • 28 benchmarks • 113 datasets
Pose Estimation is a computer vision task where the goal is to detect the position and orientation of a person or an object. Usually, this is done by predicting the location of specific keypoints like hands, head, elbows, etc. in case of Human Pose Estimation.
A common benchmark for this task is MPII Human Pose
( Image credit: Real-time 2D Multi-Person Pose Estimation on CPU: Lightweight OpenPose )
Benchmarks
These leaderboards are used to track progress in Pose Estimation
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Libraries
Use these libraries to find Pose Estimation models and implementations
MS COCO
KITTI
MPII
3DPW
AMASS
PASCAL3D+
JHMDB
DensePose
300W
LSP
Subtasks
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3D Human Pose Estimation
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Keypoint Detection
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3D Pose Estimation
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6D Pose Estimation
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6D Pose Estimation
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Hand Pose Estimation
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6D Pose Estimation using RGB
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Multi-Person Pose Estimation
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Head Pose Estimation
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Human Pose Forecasting
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6D Pose Estimation using RGBD
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Animal Pose Estimation
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Vehicle Pose Estimation
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RF-based Pose Estimation
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Car Pose Estimation
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Hand Joint Reconstruction
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Activeness Detection
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Semi-supervised 2D and 3D landmark labeling
Most implemented papers
Mask R-CNN
tensorflow/models
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ICCV 2017
Our approach efficiently detects objects in an image while simultaneously generating a high-quality segmentation mask for each instance.
Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields
ZheC/Realtime_Multi-Person_Pose_Estimation
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CVPR 2017
We present an approach to efficiently detect the 2D pose of multiple people in an image.
Convolutional Pose Machines
Pose Machines provide a sequential prediction framework for learning rich implicit spatial models.
OpenPose: Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields
CMU-Perceptual-Computing-Lab/openpose
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OpenPose: Real-time multi-person keypoint detection library for body, face, hands, and foot estimation
Stacked Hourglass Networks for Human Pose Estimation
This work introduces a novel convolutional network architecture for the task of human pose estimation.
Deep High-Resolution Representation Learning for Visual Recognition
High-resolution representations are essential for position-sensitive vision problems, such as human pose estimation, semantic segmentation, and object detection.
Deep High-Resolution Representation Learning for Human Pose Estimation
leoxiaobin/deep-high-resolution-net.pytorch
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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.
High-Resolution Representations for Labeling Pixels and Regions
leoxiaobin/deep-high-resolution-net.pytorch
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The proposed approach achieves superior results to existing single-model networks on COCO object detection.
Non-local Neural Networks
facebookresearch/video-nonlocal-net
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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
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ECCV 2018
There has been significant progress on pose estimation and increasing interests on pose tracking in recent years.
