Pose Estimation
1339 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
Latest papers with no code
Gait Recognition from Highly Compressed Videos
We systematically evaluate the performance of our artifact correction model against a range of noisy surveillance data and demonstrate that our approach not only achieves improved pose estimation on low-quality surveillance footage, but also preserves the integrity of the pose estimation on high resolution footage.
Kathakali Hand Gesture Recognition With Minimal Data
The system can work with hand-cropped or full-body images alike.
GeoReF: Geometric Alignment Across Shape Variation for Category-level Object Pose Refinement
Object pose refinement is essential for robust object pose estimation.
GaitPoint+: A Gait Recognition Network Incorporating Point Cloud Analysis and Recycling
Our approach models skeleton key points as a 3D point cloud, and employs a computational complexity-conscious 3D point processing approach to extract skeleton features, which are then combined with silhouette features for improved accuracy.
HumMUSS: Human Motion Understanding using State Space Models
Understanding human motion from video is essential for a range of applications, including pose estimation, mesh recovery and action recognition.
Invariant Kalman Filtering with Noise-Free Pseudo-Measurements
We relate the constraints to group-theoretic properties and study the behavior of the IEKF in the presence of such noise-free measurements.
LetsGo: Large-Scale Garage Modeling and Rendering via LiDAR-Assisted Gaussian Primitives
We demonstrate that the collected LiDAR point cloud by the Polar device enhances a suite of 3D Gaussian splatting algorithms for garage scene modeling and rendering.
In My Perspective, In My Hands: Accurate Egocentric 2D Hand Pose and Action Recognition
Our study aims to fill this research gap by exploring the field of 2D hand pose estimation for egocentric action recognition, making two contributions.
3D Human Scan With A Moving Event Camera
The experimental results show that the proposed method outperforms conventional frame-based methods in the estimation accuracy of both pose and body mesh.
Separated Attention: An Improved Cycle GAN Based Under Water Image Enhancement Method
In this paper we have present an improved Cycle GAN based model for under water image enhancement.
