SOTAVerified

Multi-Person Pose Estimation

Multi-person pose estimation is the task of estimating the pose of multiple people in one frame.

( Image credit: Human Pose Estimation with TensorFlow )

Papers

Showing 76–100 of 151 papers

TitleStatusHype
Texture-Based Input Feature Selection for Action Recognition—0
MDPose: Real-Time Multi-Person Pose Estimation via Mixture Density Model—0
Poses of People in Art: A Data Set for Human Pose Estimation in Digital Art History—0
Weakly Supervised 3D Multi-person Pose Estimation for Large-scale Scenes based on Monocular Camera and Single LiDAR—0
JRDB-Pose: A Large-scale Dataset for Multi-Person Pose Estimation and Tracking—0
QuickPose: Real-time Multi-view Multi-person Pose Estimation in Crowded Scenes—0
Mutual Adaptive Reasoning for Monocular 3D Multi-Person Pose Estimation—0
Self-Constrained Inference Optimization on Structural Groups for Human Pose Estimation—0
Bottom-up approaches for multi-person pose estimation and it's applications: A brief review—0
BAPose: Bottom-Up Pose Estimation with Disentangled Waterfall RepresentationsCode0
Attend to Who You Are: Supervising Self-Attention for Keypoint Detection and Instance-Aware AssociationCode0
Self-Supervision and Spatial-Sequential Attention Based Loss for Multi-Person Pose Estimation—0
Shape-aware Multi-Person Pose Estimation from Multi-View Images—0
A Benchmark for Gait Recognition under Occlusion Collected by Multi-Kinect SDAS—0
Intelligent Carpet: Inferring 3D Human Pose From Tactile Signals—0
Learning Dynamics via Graph Neural Networks for Human Pose Estimation and Tracking—0
Towards Fast and Accurate Multi-Person Pose Estimation on Mobile Devices—0
FCPose: Fully Convolutional Multi-Person Pose Estimation with Dynamic Instance-Aware Convolutions—0
Learning Spatial Context with Graph Neural Network for Multi-Person Pose GroupingCode0
SIMPLE: SIngle-network with Mimicking and Point Learning for Bottom-up Human Pose Estimation—0
A Global to Local Double Embedding Method for Multi-person Pose Estimation—0
Efficient Human Pose Estimation with Depthwise Separable Convolution and Person Centroid Guided Joint Grouping—0
ScaleNAS: One-Shot Learning of Scale-Aware Representations for Visual Recognition—0
Multi-Person Full Body Pose Estimation—0
Alleviating Human-level Shift : A Robust Domain Adaptation Method for Multi-person Pose EstimationCode0
Show:102550
← PrevPage 4 of 7Next →

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1RTMO-lmAP @0.5:0.9583.8—Unverified
2BUCTD-W48 (w/cond. input from PETR, and generative sampling)mAP @0.5:0.9578.5—Unverified
3I²R-Net (1st stage: HRFormer-B)mAP @0.5:0.9577.4—Unverified
4ED-Pose (Swin-L)mAP @0.5:0.9576.6—Unverified
5DETRPose-XmAP @0.5:0.9575.1—Unverified
6DETRPose-LmAP @0.5:0.9573.3—Unverified
7HRFormer-BmAP @0.5:0.9572.4—Unverified
8BAPose (W32)mAP @0.5:0.9572.2—Unverified
9DETRPose-MmAP @0.5:0.9572—Unverified
10TransPose-HmAP @0.5:0.9571.8—Unverified
#ModelMetricClaimedVerifiedStatus
1EvoPose2D-LTest AP76.8—Unverified
2PoseFixTest AP76.7—Unverified
3LitePose-STest AP56.7—Unverified
4RSNAP0.79—Unverified
5DarkPoseAP0.77—Unverified
6UniPoseAP0.77—Unverified
7CPN+AP0.73—Unverified
8BAPoseAP0.73—Unverified
9CenterGroupAP0.71—Unverified
10OpenPifPafAP0.71—Unverified
#ModelMetricClaimedVerifiedStatus
1SCIO (HRNet-48)AP79.2—Unverified
2HRNet-W48plusAP78.7—Unverified
3HRNet-W32AP76.2—Unverified
4ResNet50AP73.7—Unverified
5HigherHRNet (ScaleNet_P4)AP71.6—Unverified
6HigherHRNet (HR-Net-48)AP70.5—Unverified
7SMPR (HR-Net-32)AP70.2—Unverified
8PersonLabAP68.7—Unverified
9Identity Mapping HourglassAP68.1—Unverified
10SPMAP66.9—Unverified
#ModelMetricClaimedVerifiedStatus
1AlphaPoseAP82.1—Unverified
2Generative Partition NetworksAP80.4—Unverified
3SPMAP78.5—Unverified
4RefineAP78—Unverified
5Associative EmbeddingAP77.5—Unverified
6Part Affinity FieldsAP75.6—Unverified
7Articulated TrackingAP74.3—Unverified
8Local Joint-to-Person AssociationAP62.2—Unverified
9DeeperCutAP59.4—Unverified
#ModelMetricClaimedVerifiedStatus
1MIPNet (gt-bb)AP5089.7—Unverified
2I²R-Net (1st stage:TransPose-H)AP5085—Unverified
3TransPose-HAP5082.7—Unverified
4HRFormer-BAP5081.4—Unverified
5SPMAP5067.5—Unverified
6CrowdPoseAP5040.8—Unverified
7SimplePoseAP5037.4—Unverified
8Mask R-CNNAP5033.2—Unverified
#ModelMetricClaimedVerifiedStatus
1HRNet-W48plusAP79.1—Unverified
2HRNet-W32AP77.8—Unverified
3ResNet50AP75.3—Unverified
4InsPoseAP63.1—Unverified
#ModelMetricClaimedVerifiedStatus
1PoseidonMean mAP87.8—Unverified
2DCPoseMean mAP79—Unverified
3PoseWarperMean mAP78—Unverified
4RefineMean mAP73.8—Unverified
#ModelMetricClaimedVerifiedStatus
1DCPoseMean mAP79.2—Unverified
2PoseWarperMean mAP77.94—Unverified
3PoseTrackMean mAP59.4—Unverified
#ModelMetricClaimedVerifiedStatus
1DeeperCutAOP88.1—Unverified
2DeepCutAOP86.5—Unverified
3Generative Partition NetworksAP84.8—Unverified
#ModelMetricClaimedVerifiedStatus
1CMU-PoseAP0.62—Unverified
#ModelMetricClaimedVerifiedStatus
1PoseTrackMean mAP38.2—Unverified
#ModelMetricClaimedVerifiedStatus
1PoseidonMean mAP88.3—Unverified