Multi-Person Pose Estimation
81 papers with code • 11 benchmarks • 7 datasets
Multi-person pose estimation is the task of estimating the pose of multiple people in one frame.
( Image credit: Human Pose Estimation with TensorFlow )
Benchmarks
These leaderboards are used to track progress in Multi-Person Pose Estimation
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Libraries
Use these libraries to find Multi-Person Pose Estimation models and implementations
MS COCO
MPII
PoseTrack
CrowdPose
OCHuman
COCO-WholeBody
PoPArt
Latest papers with no code
DRSI-Net: Dual-Residual Spatial Interaction Network for Multi-Person Pose Estimation
Multi-person pose estimation (MPPE), which aims to locate keypoints for all persons in the frames, is an active research branch of computer vision.
Human Pose-based Estimation, Tracking and Action Recognition with Deep Learning: A Survey
This paper presents a comprehensive survey of pose-based applications utilizing deep learning, encompassing pose estimation, pose tracking, and action recognition. Pose estimation involves the determination of human joint positions from images or image sequences.
Towards Robust and Smooth 3D Multi-Person Pose Estimation from Monocular Videos in the Wild
3D pose estimation is an invaluable task in computer vision with various practical applications.
Joint Coordinate Regression and Association For Multi-Person Pose Estimation, A Pure Neural Network Approach
We introduce a novel one-stage end-to-end multi-person 2D pose estimation algorithm, known as Joint Coordinate Regression and Association (JCRA), that produces human pose joints and associations without requiring any post-processing.
Hybrid model for Single-Stage Multi-Person Pose Estimation
In general, human pose estimation methods are categorized into two approaches according to their architectures: regression (i. e., heatmap-free) and heatmap-based methods.
Global Relation Modeling and Refinement for Bottom-Up Human Pose Estimation
In this paper, we concern on the bottom-up paradigm in multi-person pose estimation (MPPE).
Texture-Based Input Feature Selection for Action Recognition
To improve the model robustness, we propose a novel method to determine the task-irrelevant content in inputs which increases the domain discrepancy.
MDPose: Real-Time Multi-Person Pose Estimation via Mixture Density Model
In this work, we propose a novel framework of single-stage instance-aware pose estimation by modeling the joint distribution of human keypoints with a mixture density model, termed as MDPose.
Poses of People in Art: A Data Set for Human Pose Estimation in Digital Art History
With the Poses of People in Art data set, we introduce the first openly licensed data set for estimating human poses in art and validating human pose estimators.
Weakly Supervised 3D Multi-person Pose Estimation for Large-scale Scenes based on Monocular Camera and Single LiDAR
Motivated by this, we propose a monocular camera and single LiDAR-based method for 3D multi-person pose estimation in large-scale scenes, which is easy to deploy and insensitive to light.
