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
Most implemented papers
Learning Delicate Local Representations for Multi-Person Pose Estimation
caiyuanhao1998/RSN
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ECCV 2020
To tackle this problem, we propose an efficient attention mechanism - Pose Refine Machine (PRM) to make a trade-off between local and global representations in output features and further refine the keypoint locations.
PersonLab: Person Pose Estimation and Instance Segmentation with a Bottom-Up, Part-Based, Geometric Embedding Model
google-coral/project-posenet
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ECCV 2018
We present a box-free bottom-up approach for the tasks of pose estimation and instance segmentation of people in multi-person images using an efficient single-shot model.
CrowdPose: Efficient Crowded Scenes Pose Estimation and A New Benchmark
open-mmlab/mmpose
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CVPR 2019
In this paper, we propose a novel and efficient method to tackle the problem of pose estimation in the crowd and a new dataset to better evaluate algorithms.
Learning Temporal Pose Estimation from Sparsely-Labeled Videos
facebookresearch/PoseWarper
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NeurIPS 2019
To reduce the need for dense annotations, we propose a PoseWarper network that leverages training videos with sparse annotations (every k frames) to learn to perform dense temporal pose propagation and estimation.
FCPose: Fully Convolutional Multi-Person Pose Estimation with Dynamic Instance-Aware Convolutions
We propose a fully convolutional multi-person pose estimation framework using dynamic instance-aware convolutions, termed FCPose.
Human Pose Regression with Residual Log-likelihood Estimation
Jeff-sjtu/res-loglikelihood-regression
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ICCV 2021
In light of this, we propose a novel regression paradigm with Residual Log-likelihood Estimation (RLE) to capture the underlying output distribution.
Explicit Box Detection Unifies End-to-End Multi-Person Pose Estimation
This paper presents a novel end-to-end framework with Explicit box Detection for multi-person Pose estimation, called ED-Pose, where it unifies the contextual learning between human-level (global) and keypoint-level (local) information.
PoseTrack: Joint Multi-Person Pose Estimation and Tracking
In this work, we introduce the challenging problem of joint multi-person pose estimation and tracking of an unknown number of persons in unconstrained videos.
PoseTrack: A Benchmark for Human Pose Estimation and Tracking
open-mmlab/mmpose
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CVPR 2018
In this work, we aim to further advance the state of the art by establishing "PoseTrack", a new large-scale benchmark for video-based human pose estimation and articulated tracking, and bringing together the community of researchers working on visual human analysis.
PifPaf: Composite Fields for Human Pose Estimation
We propose a new bottom-up method for multi-person 2D human pose estimation that is particularly well suited for urban mobility such as self-driving cars and delivery robots.
