SOTAVerified

Pose Estimation

Pose Estimation is a computer vision task where the goal is to detect the position and orientation of a person or an object. Usually, this is done by predicting the location of specific keypoints like hands, head, elbows, etc. in case of Human Pose Estimation.

A common benchmark for this task is MPII Human Pose

( Image credit: Real-time 2D Multi-Person Pose Estimation on CPU: Lightweight OpenPose )

Papers

Showing 16511700 of 4228 papers

TitleStatusHype
AutoPose: Searching Multi-Scale Branch Aggregation for Pose EstimationCode0
HoloFace: Augmenting Human-to-Human Interactions on HoloLensCode0
How Robust is 3D Human Pose Estimation to Occlusion?Code0
Automating 3D Dataset Generation with Neural Radiance FieldsCode0
Automatic vocal tract landmark localization from midsagittal MRI dataCode0
HiFi-CS: Towards Open Vocabulary Visual Grounding For Robotic Grasping Using Vision-Language ModelsCode0
Globally Tuned Cascade Pose Regression via Back Propagation with Application in 2D Face Pose Estimation and Heart Segmentation in 3D CT ImagesCode0
Automatic Social Distance Estimation From Images: Performance Evaluation, Test Benchmark, and AlgorithmCode0
Hierarchical Graph Networks for 3D Human Pose EstimationCode0
Automatic Labeling of Parkinson’s Disease Gait Videos with Weak SupervisionCode0
Single-Network Whole-Body Pose EstimationCode0
Single-Shot Multi-Person 3D Pose Estimation From Monocular RGBCode0
HEViTPose: High-Efficiency Vision Transformer for Human Pose EstimationCode0
Automatic Instrument Segmentation in Robot-Assisted Surgery Using Deep LearningCode0
Detect-and-Track: Efficient Pose Estimation in VideosCode0
Detailed, accurate, human shape estimation from clothed 3D scan sequencesCode0
Heuristic Weakly Supervised 3D Human Pose EstimationCode0
Holistic Guidance for Occluded Person Re-IdentificationCode0
Automatic Health Problem Detection from Gait Videos Using Deep Neural NetworksCode0
Design Space Exploration on Efficient and Accurate Human Pose Estimation from Sparse IMU-SensingCode0
HDG-ODE: A Hierarchical Continuous-Time Model for Human Pose ForecastingCode0
Automatic detection of faults in race walking from a smartphone camera: a comparison of an Olympic medalist and university athletesCode0
HDiffTG: A Lightweight Hybrid Diffusion-Transformer-GCN Architecture for 3D Human Pose EstimationCode0
Head Pose Estimation Based on 5D Rotation RepresentationCode0
Automatic Assessment of Infant Face and Upper-Body Symmetry as Early Signs of TorticollisCode0
HCA-Net: Hierarchical Context Attention Network for Intervertebral Disc Semantic LabelingCode0
Automatic Analysis of Human Body Representations in Western ArtCode0
Addressing the challenges of loop detection in agricultural environmentsCode0
FeatER: An Efficient Network for Human Reconstruction via Feature Map-Based TransformERCode0
Hardware-accelerated Mars Sample Localization via deep transfer learning from photorealistic simulationsCode0
Depth-Based 3D Hand Pose Estimation: From Current Achievements to Future GoalsCode0
DeProPose: Deficiency-Proof 3D Human Pose Estimation via Adaptive Multi-View FusionCode0
Hands Deep in Deep Learning for Hand Pose EstimationCode0
HandR2N2: Iterative 3D Hand Pose Estimation Using a Residual Recurrent Neural NetworkCode0
DensePose: Dense Human Pose Estimation In The WildCode0
HandDAGT: A Denoising Adaptive Graph Transformer for 3D Hand Pose EstimationCode0
DenseFusion: 6D Object Pose Estimation by Iterative Dense FusionCode0
Dense 3D Regression for Hand Pose EstimationCode0
DeLS-3D: Deep Localization and Segmentation with a 3D Semantic MapCode0
DeepSFM: Structure From Motion Via Deep Bundle AdjustmentCode0
GSTO: Gated Scale-Transfer Operation for Multi-Scale Feature Learning in Pixel LabelingCode0
Ground Plane Polling for 6DoF Pose Estimation of Objects on the RoadCode0
Joint Graph Decomposition and Node Labeling: Problem, Algorithms, ApplicationsCode0
Reproducible Evaluation of Camera Auto-Exposure Methods in the Field: Platform, Benchmark and Lessons LearnedCode0
Augmented Parallel-Pyramid Net for Attention Guided Pose-Estimation0
Combining the Silhouette and Skeleton Data for Gait Recognition0
Adaptive Wasserstein Hourglass for Weakly Supervised Hand Pose Estimation from Monocular RGB0
DeepSkeleton: Skeleton Map for 3D Human Pose Regression0
Attributed Grammars for Joint Estimation of Human Attributes, Part and Pose0
Deep Single Image Camera Calibration by Heatmap Regression to Recover Fisheye Images Under Manhattan World Assumption0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1yoloposeAP5090.3Unverified
2ViTPose (ViTAE-G, ensemble)AP81.1Unverified
3ViTPose (ViTAE-G)AP80.9Unverified
4PoseBH-HAP79.5Unverified
5UDP-Pose-PSA(384x288)AP79.5Unverified
64xRSN-50 (ensemble)AP79.2Unverified
7UDP-Pose-PSA(256x192)AP78.9Unverified
8CCM+AP78.9Unverified
94xRSN-50AP78.6Unverified
10PCT (256x256)AP78.3Unverified
#ModelMetricClaimedVerifiedStatus
1PCT (swin-l, test set)PCKh-0.594.3Unverified
2Soft-gated Skip ConnectionsPCKh-0.594.1Unverified
3Cascade Feature AggregationPCKh-0.593.9Unverified
4PCT (swin-b, test set)PCKh-0.593.8Unverified
5TransPosePCKh-0.593.5Unverified
6UniHCP (FT)PCKh-0.593.2Unverified
74xRSN-50PCKh-0.593Unverified
8UniPosePCKh-0.592.7Unverified
9MSPNPCKh-0.592.6Unverified
10Spatial ContextPCKh-0.592.5Unverified
#ModelMetricClaimedVerifiedStatus
1ViTPose (ViTAE-G, GT bounding boxes)Test AP93.3Unverified
2UniHCP (direct eval)Test AP87.4Unverified
3PoseBH-HTest AP87Unverified
4RTMPose(RTMPose-l, GT bounding boxes)Test AP80.3Unverified
5TransPose-HValidation AP62.3Unverified
6BBox-Mask-Pose 2xTest AP48.3Unverified
7BUCTD (CID-W32)Test AP47.2Unverified
8HQNet (ViT-L)Test AP45.6Unverified
9MaskPose-bTest AP45Unverified
10CID (HRNet-W48)Test AP45Unverified
#ModelMetricClaimedVerifiedStatus
1OmniPosePCK99.5Unverified
2Soft-gated Skip ConnectionsPCK94.8Unverified
3Residual Hourglass + ASR + AHOPCK94.5Unverified
4UniPosePCK94.5Unverified
5Chou et al. arXiv'17PCK94Unverified
6Pyramid Residual Modules (PRMs)PCK93.9Unverified
7Stacked hourglass + Inception-resnetPCK93.9Unverified
8Multi-Context AttentionPCK92.6Unverified
9FPDPCK90.8Unverified
10Part heatmap regression (ResNet-152)PCK90.7Unverified
#ModelMetricClaimedVerifiedStatus
1BUCTD-W48 (w/cond. input from PETR, and generative sampling)AP78.5Unverified
2ViTPose-GAP78.3Unverified
3BUCTD-W48 (w/cond. input from PETR)AP76.7Unverified
4SwinV2-L 1K-MIMAP75.5Unverified
5SwinV2-B 1K-MIMAP74.9Unverified
6BUCTD-W48AP72.9Unverified
7OpenPifPafAP70.5Unverified
8MIPNet (HRNet-W48)AP70Unverified
9KAPAO-LAP68.9Unverified
10KAPAO-MAP67.1Unverified
#ModelMetricClaimedVerifiedStatus
1CCNet (ViTPose-B_GT-bbox_256x192)AP78.1Unverified
2MogaNet-B (384x288)AP77.3Unverified
3ViTPose-B (Single-task_GT-bbox_256x192)AP77.3Unverified
4MogaNet-S (384x288)AP76.4Unverified
5Bias (HRNet_256x192)AP75.8Unverified
6ViTPose-B (Single-task_Det-bbox_256x192)AP75.8Unverified
7HRNet (256x192)AP75.3Unverified
8MogaNet-S (256x192)AP74.9Unverified
9MogaNet-T (256x192)AP73.2Unverified
10RLE (256x192)AP71.3Unverified
#ModelMetricClaimedVerifiedStatus
1Hulk(Finetune, ViT-L)AP37.1Unverified
2Hulk(Finetune, ViT-B)AP35.6Unverified
3HRFormer (HRFomer-B)AP34.4Unverified
4UniHCP (finetune)AP33.6Unverified
5HRNet (HRNet-w48 )AP33.5Unverified
6HRNet (HRNet-w32)AP32.3Unverified
7HRFormer (HRFomer-S)AP31.6Unverified
8SimpleBaseline (ResNet-152)AP29.9Unverified
9SimpleBaseline (ResNet-101)AP29.4Unverified
10SimpleBaseline (ResNet-50)AP28Unverified
#ModelMetricClaimedVerifiedStatus
1BUCTD (PETR, with generative sampling)APL83.7Unverified
2OmniPose (WASPv2)AP79.5Unverified
3MetaPrompt-SDAP79Unverified
4Hulk(Finetune, ViT-L)AP78.7Unverified
5BUCTD (PETR, with generative sampling)AP77.8Unverified
6Hulk(Finetune, ViT-B)AP77.5Unverified
7I²R-Net (1st stage:HRFormer-B)AP77.3Unverified
8PATH (Partial FT)AP77.1Unverified
9SOLIDER (swin-B)AP76.6Unverified
10PEFORMER-Xcit-dino-p8AP72.6Unverified
#ModelMetricClaimedVerifiedStatus
1GIM-DKMDUC1-Acc@0.25m,10°57.1Unverified
2GIM-LoFTRDUC1-Acc@0.25m,10°54.5Unverified
3GIM-SuperGlueDUC1-Acc@0.25m,10°53.5Unverified
4DKMDUC1-Acc@0.25m,10°51.5Unverified
5SuperGlueDUC1-Acc@0.25m,10°49Unverified
6LoFTRDUC1-Acc@0.25m,10°47.5Unverified
#ModelMetricClaimedVerifiedStatus
1AdaPoseMean mAP93.38Unverified
2DECA-D3Mean mAP88.75Unverified
3V2V-PoseNetMean mAP88.74Unverified
4A2JMean mAP88Unverified
5RENMean mAP84.9Unverified
6Multi-task learning + viewpoint invarianceMean mAP77.4Unverified
#ModelMetricClaimedVerifiedStatus
1SimpleBaseline + HANetMean PCK@0.299.6Unverified
2DeciWatchMean PCK@0.299Unverified
3LSTM PMMean PCK@0.293.6Unverified
4CPMMean PCK@0.291.9Unverified
5UniTrack_i18Mean PCK@0.280.5Unverified
#ModelMetricClaimedVerifiedStatus
14xRSN-50PCKh@0.593Unverified
2RefinePCKh@0.592.1Unverified
3EfficientPose IVPCKh@0.591.2Unverified
4OpenPosePCKh@0.588.8Unverified
5Adversarial LearningPCKh@0.588.6Unverified
#ModelMetricClaimedVerifiedStatus
1OmniPoseMean PCK@0.299.4Unverified
2UniPose-LSTMMean PCK@0.299.3Unverified
3LSTM PMMean PCK@0.297.7Unverified
4Thin-SlicingMean PCK@0.296.5Unverified
5Iqbal et al.Mean PCK@0.281.1Unverified
#ModelMetricClaimedVerifiedStatus
1DP-RCNN-DeepLab (ResNet-101)AP68Unverified