SOTAVerified

2D Human Pose Estimation

What is Human Pose Estimation? Human pose estimation is the process of estimating the configuration of the body (pose) from a single, typically monocular, image. Background. Human pose estimation is one of the key problems in computer vision that has been studied for well over 15 years. The reason for its importance is the abundance of applications that can benefit from such a technology. For example, human pose estimation allows for higher-level reasoning in the context of human-computer interaction and activity recognition; it is also one of the basic building blocks for marker-less motion capture (MoCap) technology. MoCap technology is useful for applications ranging from character animation to clinical analysis of gait pathologies.

Papers

Showing 101–118 of 118 papers

TitleStatusHype
Human Pose Estimation using Motion Priors and Ensemble Models—0
PoseFix: Model-agnostic General Human Pose Refinement NetworkCode0
Near-Optimal Representation Learning for Hierarchical Reinforcement LearningCode0
Leolani: a reference machine with a theory of mind for social communicationCode0
Part-Aligned Bilinear Representations for Person Re-identification—0
Pose2Seg: Detection Free Human Instance SegmentationCode0
LSTM Pose MachinesCode0
Alibaba at IJCNLP-2017 Task 1: Embedding Grammatical Features into LSTMs for Chinese Grammatical Error Diagnosis Task—0
DeepSkeleton: Skeleton Map for 3D Human Pose Regression—0
2D-3D Pose Consistency-based Conditional Random Fields for 3D Human Pose Estimation—0
Learning from Synthetic HumansCode0
Associative Embedding: End-to-End Learning for Joint Detection and GroupingCode0
3D Human Pose Estimation Using Convolutional Neural Networks with 2D Pose Information—0
Preconditioned Stochastic Gradient Descent—0
Beyond Tree Structure Models: A New Occlusion Aware Graphical Model for Human Pose Estimation—0
Enhanced Mixtures of Part Model for Human Pose Estimation—0
MoDeep: A Deep Learning Framework Using Motion Features for Human Pose Estimation—0
2D Human Pose Estimation: New Benchmark and State of the Art Analysis—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1RTMW-xWB70.2—Unverified
2PCNetWB66.4—Unverified
3ZoomNAS (V1.0 data)WB65.4—Unverified
4RTMPoseWB65.3—Unverified
5TCFormerWB64.2—Unverified
6ZoomNet (V1.0 data)WB63—Unverified
7Sapiens-0.3BWB62—Unverified
8ViTPose+-HWB61.2—Unverified
9Zauss et al.WB60.4—Unverified
10RTMW-mWB58—Unverified
#ModelMetricClaimedVerifiedStatus
1UniPoseAP0.76—Unverified
2RTMPose-lAP (gt bbox)0.75—Unverified
3ED-Pose (R50)AP0.72—Unverified
4ViTPose-hAP0.47—Unverified
5ViTPose-lAP0.46—Unverified
6HRNet-w48AP0.42—Unverified
7ViTpose-bAP0.41—Unverified
8HRNet-w32AP0.4—Unverified
9ViTPose-sAP0.38—Unverified
10RTMPose-sAP0.31—Unverified
#ModelMetricClaimedVerifiedStatus
1DeciWatchPCK98.8—Unverified
2PoseidonPCK97.3—Unverified
3SimplePosePCK94.4—Unverified
4DKD (ResNet50)PCK94—Unverified
5LSTM PMPCK93.6—Unverified
#ModelMetricClaimedVerifiedStatus
1SEFDTest AP44.1—Unverified
2ResNet-50Test AP30.4—Unverified
3Pose2SegTest AP23.8—Unverified
#ModelMetricClaimedVerifiedStatus
1mitsimpo10-20% Mask PSNR12—Unverified
#ModelMetricClaimedVerifiedStatus
1DA-LLPoseAP5—Unverified
#ModelMetricClaimedVerifiedStatus
1DA-LLPoseAP18.6—Unverified
#ModelMetricClaimedVerifiedStatus
1DA-LLPoseAP35.6—Unverified
#ModelMetricClaimedVerifiedStatus
1DA-LLPoseAP39.1—Unverified
#ModelMetricClaimedVerifiedStatus
1DA-LLPoseAP36.2—Unverified