Pose Estimation
1331 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
DensePose: Dense Human Pose Estimation In The Wild
facebookresearch/detectron2
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CVPR 2018
In this work, we establish dense correspondences between RGB image and a surface-based representation of the human body, a task we refer to as dense human pose estimation.
HigherHRNet: Scale-Aware Representation Learning for Bottom-Up Human Pose Estimation
HRNet/Higher-HRNet-Human-Pose-Estimation
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CVPR 2020
HigherHRNet even surpasses all top-down methods on CrowdPose test (67. 6% AP), suggesting its robustness in crowded scene.
SuperGlue: Learning Feature Matching with Graph Neural Networks
magicleap/SuperGluePretrainedNetwork
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CVPR 2020
This paper introduces SuperGlue, a neural network that matches two sets of local features by jointly finding correspondences and rejecting non-matchable points.
Visual Attention Network
Visual-Attention-Network/VAN-Classification
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In this paper, we propose a novel linear attention named large kernel attention (LKA) to enable self-adaptive and long-range correlations in self-attention while avoiding its shortcomings.
DeeperCut: A Deeper, Stronger, and Faster Multi-Person Pose Estimation Model
The goal of this paper is to advance the state-of-the-art of articulated pose estimation in scenes with multiple people.
Lite-HRNet: A Lightweight High-Resolution Network
HRNet/Lite-HRNet
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CVPR 2021
We introduce a lightweight unit, conditional channel weighting, to replace costly pointwise (1x1) convolutions in shuffle blocks.
RMPE: Regional Multi-person Pose Estimation
MVIG-SJTU/AlphaPose
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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.
ArtTrack: Articulated Multi-person Tracking in the Wild
eldar/pose-tensorflow
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CVPR 2017
In this paper we propose an approach for articulated tracking of multiple people in unconstrained videos.
A simple yet effective baseline for 3d human pose estimation
una-dinosauria/3d-pose-baseline
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ICCV 2017
Following the success of deep convolutional networks, state-of-the-art methods for 3d human pose estimation have focused on deep end-to-end systems that predict 3d joint locations given raw image pixels.
Fine-Grained Head Pose Estimation Without Keypoints
natanielruiz/deep-head-pose
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Estimating the head pose of a person is a crucial problem that has a large amount of applications such as aiding in gaze estimation, modeling attention, fitting 3D models to video and performing face alignment.
