Pose Estimation
1354 papers with code • 28 benchmarks • 114 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+
DensePose
JHMDB
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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Animal Pose Estimation
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6D Pose Estimation using RGBD
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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
SLAM for Indoor Mapping of Wide Area Construction Environments
We apply our state-of-the-art LiDAR and visual SLAM approaches and discuss the respective pros and cons of the different sensor types for trajectory estimation and dense map generation in such an environment.
DeepKalPose: An Enhanced Deep-Learning Kalman Filter for Temporally Consistent Monocular Vehicle Pose Estimation
This paper presents DeepKalPose, a novel approach for enhancing temporal consistency in monocular vehicle pose estimation applied on video through a deep-learning-based Kalman Filter.
Transformer-Based Local Feature Matching for Multimodal Image Registration
Ultrasound imaging is a cost-effective and radiation-free modality for visualizing anatomical structures in real-time, making it ideal for guiding surgical interventions.
Semi-supervised 2D Human Pose Estimation via Adaptive Keypoint Masking
Human pose estimation is a fundamental and challenging task in computer vision.
UPose3D: Uncertainty-Aware 3D Human Pose Estimation with Cross-View and Temporal Cues
At the core of our method, a pose compiler module refines predictions from a 2D keypoints estimator that operates on a single image by leveraging temporal and cross-view information.
DHRNet: A Dual-Path Hierarchical Relation Network for Multi-Person Pose Estimation
Multi-person pose estimation (MPPE) presents a formidable yet crucial challenge in computer vision.
CT-NeRF: Incremental Optimizing Neural Radiance Field and Poses with Complex Trajectory
In this pipeline, we first propose a local-global bundle adjustment under a pose graph connecting neighboring frames to enforce the consistency between poses to escape the local minima caused by only pose consistency with the scene structure.
Resampling-free Particle Filters in High-dimensions
State estimation is crucial for the performance and safety of numerous robotic applications.
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.
Spot-Compose: A Framework for Open-Vocabulary Object Retrieval and Drawer Manipulation in Point Clouds
This allows for accurate detection directly in 3D scenes, object- and environment-aware grasp prediction, as well as robust and repeatable robotic manipulation.
