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 5175 of 118 papers

TitleStatusHype
Not All Tokens Are Equal: Human-centric Visual Analysis via Token Clustering TransformerCode2
2D Human Pose Estimation: A Survey0
Study of Robust Sparsity-Aware RLS algorithms with Jointly-Optimized Parameters for Impulsive Noise Environments0
DeciWatch: A Simple Baseline for 10x Efficient 2D and 3D Pose EstimationCode1
Poseur: Direct Human Pose Regression with TransformersCode1
SmoothNet: A Plug-and-Play Network for Refining Human Poses in VideosCode1
Event Neural NetworksCode0
Rethinking Deconvolution for 2D Human Pose Estimation Light yet Accurate Model for Real-time Edge Computing0
Keypoint CommunitiesCode1
Unsupervised domain adaptation for clinician pose estimation and instance segmentation in the operating roomCode0
Greedy Offset-Guided Keypoint Grouping for Human Pose EstimationCode1
HPRNet: Hierarchical Point Regression for Whole-Body Human Pose EstimationCode1
Full-Resolution Encoder-Decoder Networks with Multi-Scale Feature Fusion for Human Pose Estimation0
Estimating Parkinsonism Severity in Natural Gait Videos of Older Adults with DementiaCode1
AGORA: Avatars in Geography Optimized for Regression AnalysisCode1
Learning to Estimate Robust 3D Human Mesh from In-the-Wild Crowded ScenesCode1
Location-Sensitive Visual Recognition with Cross-IOU LossCode1
What is it Like to Be a Bot: Simulated, Situated, Structurally Coherent Qualia (S3Q) Theory of Consciousness0
On the role of depth predictions for 3D human pose estimation0
Multi-Instance Pose Networks: Rethinking Top-Down Pose EstimationCode1
Removing the Bias of Integral Pose Regression0
Contextual Graph Reasoning Networks0
Deep Learning-Based Human Pose Estimation: A SurveyCode1
Learning Heatmap-Style Jigsaw Puzzles Provides Good Pretraining for 2D Human Pose Estimation0
Efficient Human Pose Estimation with Depthwise Separable Convolution and Person Centroid Guided Joint Grouping0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1RTMW-xWB70.2Unverified
2PCNetWB66.4Unverified
3ZoomNAS (V1.0 data)WB65.4Unverified
4RTMPoseWB65.3Unverified
5TCFormerWB64.2Unverified
6ZoomNet (V1.0 data)WB63Unverified
7Sapiens-0.3BWB62Unverified
8ViTPose+-HWB61.2Unverified
9Zauss et al.WB60.4Unverified
10RTMW-mWB58Unverified
#ModelMetricClaimedVerifiedStatus
1UniPoseAP0.76Unverified
2RTMPose-lAP (gt bbox)0.75Unverified
3ED-Pose (R50)AP0.72Unverified
4ViTPose-hAP0.47Unverified
5ViTPose-lAP0.46Unverified
6HRNet-w48AP0.42Unverified
7ViTpose-bAP0.41Unverified
8HRNet-w32AP0.4Unverified
9ViTPose-sAP0.38Unverified
10RTMPose-sAP0.31Unverified
#ModelMetricClaimedVerifiedStatus
1DeciWatchPCK98.8Unverified
2PoseidonPCK97.3Unverified
3SimplePosePCK94.4Unverified
4DKD (ResNet50)PCK94Unverified
5LSTM PMPCK93.6Unverified
#ModelMetricClaimedVerifiedStatus
1SEFDTest AP44.1Unverified
2ResNet-50Test AP30.4Unverified
3Pose2SegTest AP23.8Unverified
#ModelMetricClaimedVerifiedStatus
1mitsimpo10-20% Mask PSNR12Unverified
#ModelMetricClaimedVerifiedStatus
1DA-LLPoseAP5Unverified
#ModelMetricClaimedVerifiedStatus
1DA-LLPoseAP18.6Unverified
#ModelMetricClaimedVerifiedStatus
1DA-LLPoseAP35.6Unverified
#ModelMetricClaimedVerifiedStatus
1DA-LLPoseAP39.1Unverified
#ModelMetricClaimedVerifiedStatus
1DA-LLPoseAP36.2Unverified