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
AdaptivePose++: A Powerful Single-Stage Network for Multi-Person Pose Regression
With the proposed body representation, we further deliver a compact single-stage multi-person pose regression network, termed as AdaptivePose.
Snipper: A Spatiotemporal Transformer for Simultaneous Multi-Person 3D Pose Estimation Tracking and Forecasting on a Video Snippet
jimmyzou/snipper
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In this paper, we propose Snipper, a unified framework to perform multi-person 3D pose estimation, tracking, and motion forecasting simultaneously in a single stage.
I^2R-Net: Intra- and Inter-Human Relation Network for Multi-Person Pose Estimation
leijue222/Intra-and-Inter-Human-Relation-Network-for-MPEE
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In this paper, we present the Intra- and Inter-Human Relation Networks (I^2R-Net) for Multi-Person Pose Estimation.
Lite Pose: Efficient Architecture Design for 2D Human Pose Estimation
mit-han-lab/litepose
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CVPR 2022
Inspired by this finding, we design LitePose, an efficient single-branch architecture for pose estimation, and introduce two simple approaches to enhance the capacity of LitePose, including Fusion Deconv Head and Large Kernel Convs.
Dual networks based 3D Multi-Person Pose Estimation from Monocular Video
3dpose/3D-Multi-Person-Pose
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Most of the methods focus on single persons, which estimate the poses in the person-centric coordinates, i. e., the coordinates based on the center of the target person.
YOLO-Pose: Enhancing YOLO for Multi Person Pose Estimation Using Object Keypoint Similarity Loss
texasinstruments/edgeai-yolov5
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All experiments and results reported in this paper are without any test time augmentation, unlike traditional approaches that use flip-test and multi-scale testing to boost performance.
End-to-End Multi-Person Pose Estimation With Transformers
hikvision-research/opera
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CVPR 2022
Current methods of multi-person pose estimation typically treat the localization and association of body joints separately.
PoseTrack21: A Dataset for Person Search, Multi-Object Tracking and Multi-Person Pose Tracking
andoer/posetrack21
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CVPR 2022
Current research evaluates person search, multi-object tracking and multi-person pose estimation as separate tasks and on different datasets although these tasks are very akin to each other and comprise similar sub-tasks, e. g. person detection or appearance-based association of detected persons.
Contextual Instance Decoupling for Robust Multi-Person Pose Estimation
Instead of relying on person bounding boxes to spatially differentiate persons, CID decouples persons in an image into multiple instance-aware feature maps.
AdaptivePose: Human Parts as Adaptive Points
Multi-person pose estimation methods generally follow top-down and bottom-up paradigms, both of which can be considered as two-stage approaches thus leading to the high computation cost and low efficiency.
