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 901–950 of 4228 papers

TitleStatusHype
ArtiBoost: Boosting Articulated 3D Hand-Object Pose Estimation via Online Exploration and SynthesisCode1
CullNet: Calibrated and Pose Aware Confidence Scores for Object Pose EstimationCode1
Monocular 3D Human Pose Estimation for Sports Broadcasts using Partial Sports Field RegistrationCode1
Learning Accurate Dense Correspondences and When to Trust ThemCode1
Deep Orientation Uncertainty Learning based on a Bingham LossCode1
Learning Affine Correspondences by Integrating Geometric ConstraintsCode1
Mobile-URSONet: an Embeddable Neural Network for Onboard Spacecraft Pose EstimationCode1
Learning Delicate Local Representations for Multi-Person Pose EstimationCode1
MoCapDeform: Monocular 3D Human Motion Capture in Deformable ScenesCode1
Learning from Simulated and Unsupervised Images through Adversarial TrainingCode1
Activating Self-Attention for Multi-Scene Absolute Pose RegressionCode1
Learning How To Robustly Estimate Camera Pose in Endoscopic VideosCode1
Line-based 6-DoF Object Pose Estimation and Tracking With an Event CameraCode1
Modeling Uncertain Feature Representation for Domain GeneralizationCode1
ARTS: Semi-Analytical Regressor using Disentangled Skeletal Representations for Human Mesh Recovery from VideosCode1
SiMHand: Mining Similar Hands for Large-Scale 3D Hand Pose Pre-trainingCode1
Dancing to MusicCode1
DART: Articulated Hand Model with Diverse Accessories and Rich TexturesCode1
Learning Structure-from-Motion with Graph Attention NetworksCode1
Simultaneous Multi-View Camera Pose Estimation and Object Tracking with Square Planar MarkersCode1
Monocular 3D Multi-Person Pose Estimation by Integrating Top-Down and Bottom-Up NetworksCode1
Single-to-Dual-View Adaptation for Egocentric 3D Hand Pose EstimationCode1
Learning Structure-Supporting Dependencies via Keypoint Interactive Transformer for General Mammal Pose EstimationCode1
DCL-Net: Deep Correspondence Learning Network for 6D Pose EstimationCode1
Energy-Based Models for Deep Probabilistic RegressionCode1
D&D: Learning Human Dynamics from Dynamic CameraCode1
MotionBEV: Attention-Aware Online LiDAR Moving Object Segmentation with Bird's Eye View based Appearance and Motion FeaturesCode1
milliFlow: Scene Flow Estimation on mmWave Radar Point Cloud for Human Motion SensingCode1
A Simple Method to Boost Human Pose Estimation Accuracy by Correcting the Joint Regressor for the Human3.6m DatasetCode1
Learning To Detect Scene Landmarks for Camera LocalizationCode1
A simple yet effective baseline for 3d human pose estimationCode1
Learning to Estimate 6DoF Pose from Limited Data: A Few-Shot, Generalizable Approach using RGB ImagesCode1
DeciWatch: A Simple Baseline for 10x Efficient 2D and 3D Pose EstimationCode1
Active Learning for Bayesian 3D Hand Pose EstimationCode1
Learning to Predict 3D Lane Shape and Camera Pose from a Single Image via Geometry ConstraintsCode1
MIMIC: Masked Image Modeling with Image CorrespondencesCode1
MEVID: Multi-view Extended Videos with Identities for Video Person Re-IdentificationCode1
MHFormer: Multi-Hypothesis Transformer for 3D Human Pose EstimationCode1
Automatically Annotating Indoor Images with CAD Models via RGB-D ScansCode1
Rethinking the Data Annotation Process for Multi-view 3D Pose Estimation with Active Learning and Self-TrainingCode1
Snipper: A Spatiotemporal Transformer for Simultaneous Multi-Person 3D Pose Estimation Tracking and Forecasting on a Video SnippetCode1
SoloPose: One-Shot Kinematic 3D Human Pose Estimation with Video Data AugmentationCode1
Deep learning and machine learning techniques for head pose estimation: a surveyCode1
Leveraging Anthropometric Measurements to Improve Human Mesh Estimation and Ensure Consistent Body ShapesCode1
Deep Learning-Based Human Pose Estimation: A SurveyCode1
Level-S^2fM: Structure from Motion on Neural Level Set of Implicit SurfacesCode1
Deep Keypoint-Based Camera Pose Estimation with Geometric ConstraintsCode1
Spatio-temporal MLP-graph network for 3D human pose estimationCode1
AutoLink: Self-supervised Learning of Human Skeletons and Object Outlines by Linking KeypointsCode1
Deep Label Distribution Learning with Label AmbiguityCode1
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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