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 101118 of 118 papers

TitleStatusHype
Boosting Semi-Supervised 2D Human Pose Estimation by Revisiting Data Augmentation and Consistency TrainingCode0
Efficient, Self-Supervised Human Pose Estimation with Inductive Prior TuningCode0
AutoPose: Searching Multi-Scale Branch Aggregation for Pose EstimationCode0
Semi-supervised Human Pose Estimation in Art-historical ImagesCode0
Domain-Adaptive 2D Human Pose Estimation via Dual Teachers in Extremely Low-Light ConditionsCode0
Data-Free Backbone Fine-Tuning for Pruned Neural NetworksCode0
导师评价网数据已在Github备份,可直接检索导师个人信息!Code0
PoseFix: Model-agnostic General Human Pose Refinement NetworkCode0
Pose2Seg: Detection Free Human Instance SegmentationCode0
2D Human Pose Estimation: New Benchmark and State of the Art AnalysisCode0
PifPaf: Composite Fields for Human Pose EstimationCode0
Near-Optimal Representation Learning for Hierarchical Reinforcement LearningCode0
Preconditioned Stochastic Gradient DescentCode0
Associative Embedding: End-to-End Learning for Joint Detection and GroupingCode0
LSTM Pose MachinesCode0
Leolani: a reference machine with a theory of mind for social communicationCode0
An Animation-based Augmentation Approach for Action Recognition from Discontinuous VideoCode0
HigherHRNet: Scale-Aware Representation Learning for Bottom-Up Human Pose EstimationCode0
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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