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 751–800 of 4228 papers

TitleStatusHype
Anatomy-aware 3D Human Pose Estimation with Bone-based Pose DecompositionCode1
3D/2D Registration of Angiograms using Silhouette-based Differentiable RenderingCode1
Anatomy-guided domain adaptation for 3D in-bed human pose estimationCode1
Domain Knowledge-Informed Self-Supervised Representations for Workout Form AssessmentCode1
Location-Sensitive Visual Recognition with Cross-IOU LossCode1
CloudAAE: Learning 6D Object Pose Regression with On-line Data Synthesis on Point CloudsCode1
Double-chain Constraints for 3D Human Pose Estimation in Images and VideosCode1
Loose Inertial Poser: Motion Capture with IMU-attached Loose-Wear JacketCode1
A Visual Navigation Perspective for Category-Level Object Pose EstimationCode1
Mask2Hand: Learning to Predict the 3D Hand Pose and Shape from ShadowCode1
Mask R-CNNCode1
MatchFormer: Interleaving Attention in Transformers for Feature MatchingCode1
A Dual-Augmentor Framework for Domain Generalization in 3D Human Pose EstimationCode1
DITTO: Demonstration Imitation by Trajectory TransformationCode1
Enhancing Scene Coordinate Regression with Efficient Keypoint Detection and Sequential InformationCode1
Coarse-to-Fine Volumetric Prediction for Single-Image 3D Human PoseCode1
Distribution-Aware Single-Stage Models for Multi-Person 3D Pose EstimationCode1
CullNet: Calibrated and Pose Aware Confidence Scores for Object Pose EstimationCode1
CoDiff: Conditional Diffusion Model for Collaborative 3D Object DetectionCode1
Co-Evolution of Pose and Mesh for 3D Human Body Estimation from VideoCode1
Coherent Reconstruction of Multiple Humans from a Single ImageCode1
Accuracy on the Line: On the Strong Correlation Between Out-of-Distribution and In-Distribution GeneralizationCode1
DistilPose: Tokenized Pose Regression with Heatmap DistillationCode1
MIMIC: Masked Image Modeling with Image CorrespondencesCode1
Dite-HRNet: Dynamic Lightweight High-Resolution Network for Human Pose EstimationCode1
Do Different Tracking Tasks Require Different Appearance Models?Code1
Modeling Uncertain Feature Representation for Domain GeneralizationCode1
Modulated Graph Convolutional Network for 3D Human Pose EstimationCode1
DProST: Dynamic Projective Spatial Transformer Network for 6D Pose EstimationCode1
Monocular 3D Human Pose Estimation for Sports Broadcasts using Partial Sports Field RegistrationCode1
Animatable Neural Radiance Fields from Monocular RGB VideosCode1
Monocular Real-time Hand Shape and Motion Capture using Multi-modal DataCode1
DVMNet++: Rethinking Relative Pose Estimation for Unseen ObjectsCode1
CRT-6D: Fast 6D Object Pose Estimation with Cascaded Refinement TransformersCode1
Ego-Body Pose Estimation via Ego-Head Pose EstimationCode1
DiffusionRegPose: Enhancing Multi-Person Pose Estimation using a Diffusion-Based End-to-End Regression ApproachCode1
Direct Multi-view Multi-person 3D Pose EstimationCode1
Diffusion-Driven Self-Supervised Learning for Shape Reconstruction and Pose EstimationCode1
DiffusionNOCS: Managing Symmetry and Uncertainty in Sim2Real Multi-Modal Category-level Pose EstimationCode1
Comparative Evaluation of 3D Reconstruction Methods for Object Pose EstimationCode1
Accurate, Low-Latency Visual Perception for Autonomous Racing:Challenges, Mechanisms, and Practical SolutionsCode1
MRC-Net: 6-DoF Pose Estimation with MultiScale Residual CorrelationCode1
DirectPose: Direct End-to-End Multi-Person Pose EstimationCode1
Multi-Instance Pose Networks: Rethinking Top-Down Pose EstimationCode1
3D Human Pose Estimation With Spatio-Temporal Criss-Cross AttentionCode1
Multi-Person Absolute 3D Human Pose Estimation with Weak Depth SupervisionCode1
Compressed Volumetric Heatmaps for Multi-Person 3D Pose EstimationCode1
Diffusion-Based 3D Human Pose Estimation with Multi-Hypothesis AggregationCode1
NeFSAC: Neurally Filtered Minimal SamplesCode1
Differentiable Multi-Granularity Human Representation Learning for Instance-Aware Human Semantic ParsingCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1yoloposeAP5090.3—Unverified
2ViTPose (ViTAE-G, ensemble)AP81.1—Unverified
3ViTPose (ViTAE-G)AP80.9—Unverified
4PoseBH-HAP79.5—Unverified
5UDP-Pose-PSA(384x288)AP79.5—Unverified
64xRSN-50 (ensemble)AP79.2—Unverified
7UDP-Pose-PSA(256x192)AP78.9—Unverified
8CCM+AP78.9—Unverified
94xRSN-50AP78.6—Unverified
10PCT (256x256)AP78.3—Unverified
#ModelMetricClaimedVerifiedStatus
1PCT (swin-l, test set)PCKh-0.594.3—Unverified
2Soft-gated Skip ConnectionsPCKh-0.594.1—Unverified
3Cascade Feature AggregationPCKh-0.593.9—Unverified
4PCT (swin-b, test set)PCKh-0.593.8—Unverified
5TransPosePCKh-0.593.5—Unverified
6UniHCP (FT)PCKh-0.593.2—Unverified
74xRSN-50PCKh-0.593—Unverified
8UniPosePCKh-0.592.7—Unverified
9MSPNPCKh-0.592.6—Unverified
10Spatial ContextPCKh-0.592.5—Unverified
#ModelMetricClaimedVerifiedStatus
1ViTPose (ViTAE-G, GT bounding boxes)Test AP93.3—Unverified
2UniHCP (direct eval)Test AP87.4—Unverified
3PoseBH-HTest AP87—Unverified
4RTMPose(RTMPose-l, GT bounding boxes)Test AP80.3—Unverified
5TransPose-HValidation AP62.3—Unverified
6BBox-Mask-Pose 2xTest AP48.3—Unverified
7BUCTD (CID-W32)Test AP47.2—Unverified
8HQNet (ViT-L)Test AP45.6—Unverified
9MaskPose-bTest AP45—Unverified
10CID (HRNet-W48)Test AP45—Unverified
#ModelMetricClaimedVerifiedStatus
1OmniPosePCK99.5—Unverified
2Soft-gated Skip ConnectionsPCK94.8—Unverified
3Residual Hourglass + ASR + AHOPCK94.5—Unverified
4UniPosePCK94.5—Unverified
5Chou et al. arXiv'17PCK94—Unverified
6Pyramid Residual Modules (PRMs)PCK93.9—Unverified
7Stacked hourglass + Inception-resnetPCK93.9—Unverified
8Multi-Context AttentionPCK92.6—Unverified
9FPDPCK90.8—Unverified
10Part heatmap regression (ResNet-152)PCK90.7—Unverified
#ModelMetricClaimedVerifiedStatus
1BUCTD-W48 (w/cond. input from PETR, and generative sampling)AP78.5—Unverified
2ViTPose-GAP78.3—Unverified
3BUCTD-W48 (w/cond. input from PETR)AP76.7—Unverified
4SwinV2-L 1K-MIMAP75.5—Unverified
5SwinV2-B 1K-MIMAP74.9—Unverified
6BUCTD-W48AP72.9—Unverified
7OpenPifPafAP70.5—Unverified
8MIPNet (HRNet-W48)AP70—Unverified
9KAPAO-LAP68.9—Unverified
10KAPAO-MAP67.1—Unverified
#ModelMetricClaimedVerifiedStatus
1CCNet (ViTPose-B_GT-bbox_256x192)AP78.1—Unverified
2MogaNet-B (384x288)AP77.3—Unverified
3ViTPose-B (Single-task_GT-bbox_256x192)AP77.3—Unverified
4MogaNet-S (384x288)AP76.4—Unverified
5Bias (HRNet_256x192)AP75.8—Unverified
6ViTPose-B (Single-task_Det-bbox_256x192)AP75.8—Unverified
7HRNet (256x192)AP75.3—Unverified
8MogaNet-S (256x192)AP74.9—Unverified
9MogaNet-T (256x192)AP73.2—Unverified
10RLE (256x192)AP71.3—Unverified
#ModelMetricClaimedVerifiedStatus
1Hulk(Finetune, ViT-L)AP37.1—Unverified
2Hulk(Finetune, ViT-B)AP35.6—Unverified
3HRFormer (HRFomer-B)AP34.4—Unverified
4UniHCP (finetune)AP33.6—Unverified
5HRNet (HRNet-w48 )AP33.5—Unverified
6HRNet (HRNet-w32)AP32.3—Unverified
7HRFormer (HRFomer-S)AP31.6—Unverified
8SimpleBaseline (ResNet-152)AP29.9—Unverified
9SimpleBaseline (ResNet-101)AP29.4—Unverified
10SimpleBaseline (ResNet-50)AP28—Unverified
#ModelMetricClaimedVerifiedStatus
1BUCTD (PETR, with generative sampling)APL83.7—Unverified
2OmniPose (WASPv2)AP79.5—Unverified
3MetaPrompt-SDAP79—Unverified
4Hulk(Finetune, ViT-L)AP78.7—Unverified
5BUCTD (PETR, with generative sampling)AP77.8—Unverified
6Hulk(Finetune, ViT-B)AP77.5—Unverified
7I²R-Net (1st stage:HRFormer-B)AP77.3—Unverified
8PATH (Partial FT)AP77.1—Unverified
9SOLIDER (swin-B)AP76.6—Unverified
10PEFORMER-Xcit-dino-p8AP72.6—Unverified
#ModelMetricClaimedVerifiedStatus
1GIM-DKM[email protected],10°57.1—Unverified
2GIM-LoFTR[email protected],10°54.5—Unverified
3GIM-SuperGlue[email protected],10°53.5—Unverified
4DKM[email protected],10°51.5—Unverified
5SuperGlue[email protected],10°49—Unverified
6LoFTR[email protected],10°47.5—Unverified
#ModelMetricClaimedVerifiedStatus
1AdaPoseMean mAP93.38—Unverified
2DECA-D3Mean mAP88.75—Unverified
3V2V-PoseNetMean mAP88.74—Unverified
4A2JMean mAP88—Unverified
5RENMean mAP84.9—Unverified
6Multi-task learning + viewpoint invarianceMean mAP77.4—Unverified
#ModelMetricClaimedVerifiedStatus
1SimpleBaseline + HANetMean [email protected]99.6—Unverified
2DeciWatchMean [email protected]99—Unverified
3LSTM PMMean [email protected]93.6—Unverified
4CPMMean [email protected]91.9—Unverified
5UniTrack_i18Mean [email protected]80.5—Unverified
#ModelMetricClaimedVerifiedStatus
14xRSN-50[email protected]93—Unverified
2Refine[email protected]92.1—Unverified
3EfficientPose IV[email protected]91.2—Unverified
4OpenPose[email protected]88.8—Unverified
5Adversarial Learning[email protected]88.6—Unverified
#ModelMetricClaimedVerifiedStatus
1OmniPoseMean [email protected]99.4—Unverified
2UniPose-LSTMMean [email protected]99.3—Unverified
3LSTM PMMean [email protected]97.7—Unverified
4Thin-SlicingMean [email protected]96.5—Unverified
5Iqbal et al.Mean [email protected]81.1—Unverified
#ModelMetricClaimedVerifiedStatus
1DP-RCNN-DeepLab (ResNet-101)AP68—Unverified