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 21512200 of 4228 papers

TitleStatusHype
Compact 3D Gaussian Splatting For Dense Visual SLAM0
Comparative Study of Parameter Selection for Enhanced Edge Inference for a Multi-Output Regression model for Head Pose Estimation0
Comparison of marker-less 2D image-based methods for infant pose estimation0
Comparison of Visual Trackers for Biomechanical Analysis of Running0
Composite Localization for Human Pose Estimation0
Computationally Efficient Regression on a Dependency Graph for Human Pose Estimation0
Computer methods for 3D motion tracking in real-time0
Computer vision tasks for intelligent aerospace missions: An overview0
Computing Egomotion with Local Loop Closures for Egocentric Videos0
Concurrent Segmentation and Localization for Tracking of Surgical Instruments0
CondiMen: Conditional Multi-Person Mesh Recovery0
Condition numbers in multiview geometry, instability in relative pose estimation, and RANSAC0
Conscious Inference for Object Detection0
Conservative Wasserstein Training for Pose Estimation0
Instance Scale Normalization for image understanding0
Constrained Low-Rank Learning Using Least Squares-Based Regularization0
ContactArt: Learning 3D Interaction Priors for Category-level Articulated Object and Hand Poses Estimation0
Context-aware 6D Pose Estimation of Known Objects using RGB-D data0
Context-Aware Deep Spatio-Temporal Network for Hand Pose Estimation from Depth Images0
Contextual Graph Reasoning Networks0
Continual Human Pose Estimation for Incremental Integration of Keypoints and Pose Variations0
Continuous close-range 3D object pose estimation0
Continuous hand-eye calibration using 3D points0
Continuous Normalizing Flows for Uncertainty-Aware Human Pose Estimation0
Continuous Pose Estimation With a Spatial Ensemble of Fisher Regressors0
Contour-based Hand Pose Recognition for Sign Language Recognition0
Convex Relaxations of SE(2) and SE(3) for Visual Pose Estimation0
ConvNets with Smooth Adaptive Activation Functions for Regression0
Convolutional Models for Joint Object Categorization and Pose Estimation0
Convolutional Networks for Object Category and 3D Pose Estimation from 2D Images0
Convolutional Neural Networks for joint object detection and pose estimation: A comparative study0
ConvPoseCNN2: Prediction and Refinement of Dense 6D Object Poses0
ConvPoseCNN: Dense Convolutional 6D Object Pose Estimation0
Co-op: Correspondence-based Novel Object Pose Estimation0
Cooperative Probabilistic Trajectory Forecasting under Occlusion0
COPE: End-to-end trainable Constant Runtime Object Pose Estimation0
CoPR: Towards Accurate Visual Localization With Continuous Place-descriptor Regression0
CordViP: Correspondence-based Visuomotor Policy for Dexterous Manipulation in Real-World0
Coresets for Kinematic Data: From Theorems to Real-Time Systems0
CorNet: Generic 3D Corners for 6D Pose Estimation of New Objects without Retraining0
Corr2Distrib: Making Ambiguous Correspondences an Ally to Predict Reliable 6D Pose Distributions0
Correction to:"Position estimation from direction or range measurements"0
Correspondence-Free Pose Estimation with Patterns: A Unified Approach for Multi-Dimensional Vision0
Correspondence-Guided SfM-Free 3D Gaussian Splatting for NVS0
Correspondences of the Third Kind: Camera Pose Estimation from Object Reflection0
Counter-Hypothetical Particle Filters for Single Object Pose Tracking0
Coupled Recurrent Network (CRN)0
Covariance Intersection-based Invariant Kalman Filtering(DInCIKF) for Distributed Pose Estimation0
CoVisPose: Co-Visibility Pose Transformer for Wide-Baseline Relative Pose Estimation in 360◦ Indoor Panoramas0
CPFES: Physical Fitness Evaluation Based on Canadian Agility and Movement Skill Assessment0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1yoloposeAP5090.3Unverified
2ViTPose (ViTAE-G, ensemble)AP81.1Unverified
3ViTPose (ViTAE-G)AP80.9Unverified
4UDP-Pose-PSA(384x288)AP79.5Unverified
5PoseBH-HAP79.5Unverified
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
9CID (HRNet-W48)Test AP45Unverified
10MaskPose-bTest AP45Unverified
#ModelMetricClaimedVerifiedStatus
1OmniPosePCK99.5Unverified
2Soft-gated Skip ConnectionsPCK94.8Unverified
3UniPosePCK94.5Unverified
4Residual Hourglass + ASR + AHOPCK94.5Unverified
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