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

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Showing 1301–1325 of 4228 papers

Title	Date	Tasks	Status
CordViP: Correspondence-based Visuomotor Policy for Dexterous Manipulation in Real-World	Feb 12, 2025	6D Pose EstimationImitation Learning	—Unverified
Fit-NGP: Fitting Object Models to Neural Graphics Primitives	Jan 4, 2024	ObjectPose Estimation	—Unverified
CoPR: Towards Accurate Visual Localization With Continuous Place-descriptor Regression	Apr 14, 2023	Image-Based LocalizationPose Estimation	—Unverified
COPE: End-to-end trainable Constant Runtime Object Pose Estimation	Aug 18, 2022	6D Pose Estimation using RGBObject	—Unverified
3D-Aware Object Localization using Gaussian Implicit Occupancy Function	Mar 3, 2023	ObjectObject Localization	—Unverified
Cooperative Probabilistic Trajectory Forecasting under Occlusion	Dec 6, 2023	Pose EstimationTrajectory Forecasting	—Unverified
A Projective Geometric View for 6D Pose Estimation in mmWave MIMO Systems	Feb 1, 2023	6D Pose EstimationPose Estimation	—Unverified
Grand Challenge of 106-Point Facial Landmark Localization	May 9, 2019	Face AlignmentFace Recognition	—Unverified
E^3NeRF: Efficient Event-Enhanced Neural Radiance Fields from Blurry Images	Aug 3, 2024	Camera Pose EstimationNeRF	—Unverified
3D Landmark Detection on Human Point Clouds: A Benchmark and A Dual Cascade Point Transformer Framework	Jan 14, 2024	DecoderPose Estimation	—Unverified
Co-op: Correspondence-based Novel Object Pose Estimation	Mar 22, 2025	ObjectPose Estimation	—Unverified
ConvPoseCNN: Dense Convolutional 6D Object Pose Estimation	Dec 16, 2019	6D Pose Estimation using RGBClustering	—Unverified
A Probabilistic Relaxation of the Two-Stage Object Pose Estimation Paradigm	Jun 1, 2023	Pose Estimationvalid	—Unverified
First-Person Pose Recognition Using Egocentric Workspaces	Jun 1, 2015	Pose Estimation	—Unverified
Fish2Mesh Transformer: 3D Human Mesh Recovery from Egocentric Vision	Mar 8, 2025	Human Mesh RecoveryPose Estimation	—Unverified
Five Shades of Grey for Fast and Reliable Camera Pose Estimation	Jun 1, 2013	Camera Pose EstimationPose Estimation	—Unverified
ConvPoseCNN2: Prediction and Refinement of Dense 6D Object Poses	May 23, 2022	ObjectPose Estimation	—Unverified
A Comparison of Directional Distances for Hand Pose Estimation	Apr 3, 2017	BenchmarkingHand Pose Estimation	—Unverified
Convolutional Neural Networks for joint object detection and pose estimation: A comparative study	Dec 22, 2014	General ClassificationObject	—Unverified
Convolutional Networks for Object Category and 3D Pose Estimation from 2D Images	Nov 20, 2017	3D Pose EstimationObject	—Unverified
Convolutional Models for Joint Object Categorization and Pose Estimation	Nov 16, 2015	ObjectObject Categorization	—Unverified
Approaches, Challenges, and Applications for Deep Visual Odometry: Toward to Complicated and Emerging Areas	Sep 6, 2020	Deep LearningDepth Estimation	—Unverified
Applications of Deep Learning for Top-View Omnidirectional Imaging: A Survey	Apr 17, 2023	Activity RecognitionDeep Learning	—Unverified
ConvNets with Smooth Adaptive Activation Functions for Regression	Jan 1, 2017	Age And Gender ClassificationPose Estimation	—Unverified
LocPoseNet: Robust Location Prior for Unseen Object Pose Estimation	Nov 29, 2022	6D Pose Estimation6D Pose Estimation using RGB	—Unverified

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All datasets COCO test-dev MPII Human Pose OCHuman Leeds Sports Poses CrowdPose COCO val2017 AIC COCO (Common Objects in Context)InLoc ITOP front-view J-HMDB MPII Single Person

Benchmark Results

#	Model	Metric	Claimed	Verified	Status
1	yolopose	AP50	90.3	—	Unverified
2	ViTPose (ViTAE-G, ensemble)	AP	81.1	—	Unverified
3	ViTPose (ViTAE-G)	AP	80.9	—	Unverified
4	PoseBH-H	AP	79.5	—	Unverified
5	UDP-Pose-PSA(384x288)	AP	79.5	—	Unverified
6	4xRSN-50 (ensemble)	AP	79.2	—	Unverified
7	UDP-Pose-PSA(256x192)	AP	78.9	—	Unverified
8	CCM+	AP	78.9	—	Unverified
9	4xRSN-50	AP	78.6	—	Unverified
10	PCT (256x256)	AP	78.3	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	PCT (swin-l, test set)	PCKh-0.5	94.3	—	Unverified
2	Soft-gated Skip Connections	PCKh-0.5	94.1	—	Unverified
3	Cascade Feature Aggregation	PCKh-0.5	93.9	—	Unverified
4	PCT (swin-b, test set)	PCKh-0.5	93.8	—	Unverified
5	TransPose	PCKh-0.5	93.5	—	Unverified
6	UniHCP (FT)	PCKh-0.5	93.2	—	Unverified
7	4xRSN-50	PCKh-0.5	93	—	Unverified
8	UniPose	PCKh-0.5	92.7	—	Unverified
9	MSPN	PCKh-0.5	92.6	—	Unverified
10	Spatial Context	PCKh-0.5	92.5	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	ViTPose (ViTAE-G, GT bounding boxes)	Test AP	93.3	—	Unverified
2	UniHCP (direct eval)	Test AP	87.4	—	Unverified
3	PoseBH-H	Test AP	87	—	Unverified
4	RTMPose(RTMPose-l, GT bounding boxes)	Test AP	80.3	—	Unverified
5	TransPose-H	Validation AP	62.3	—	Unverified
6	BBox-Mask-Pose 2x	Test AP	48.3	—	Unverified
7	BUCTD (CID-W32)	Test AP	47.2	—	Unverified
8	HQNet (ViT-L)	Test AP	45.6	—	Unverified
9	MaskPose-b	Test AP	45	—	Unverified
10	CID (HRNet-W48)	Test AP	45	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	OmniPose	PCK	99.5	—	Unverified
2	Soft-gated Skip Connections	PCK	94.8	—	Unverified
3	Residual Hourglass + ASR + AHO	PCK	94.5	—	Unverified
4	UniPose	PCK	94.5	—	Unverified
5	Chou et al. arXiv'17	PCK	94	—	Unverified
6	Pyramid Residual Modules (PRMs)	PCK	93.9	—	Unverified
7	Stacked hourglass + Inception-resnet	PCK	93.9	—	Unverified
8	Multi-Context Attention	PCK	92.6	—	Unverified
9	FPD	PCK	90.8	—	Unverified
10	Part heatmap regression (ResNet-152)	PCK	90.7	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	BUCTD-W48 (w/cond. input from PETR, and generative sampling)	AP	78.5	—	Unverified
2	ViTPose-G	AP	78.3	—	Unverified
3	BUCTD-W48 (w/cond. input from PETR)	AP	76.7	—	Unverified
4	SwinV2-L 1K-MIM	AP	75.5	—	Unverified
5	SwinV2-B 1K-MIM	AP	74.9	—	Unverified
6	BUCTD-W48	AP	72.9	—	Unverified
7	OpenPifPaf	AP	70.5	—	Unverified
8	MIPNet (HRNet-W48)	AP	70	—	Unverified
9	KAPAO-L	AP	68.9	—	Unverified
10	KAPAO-M	AP	67.1	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	CCNet (ViTPose-B_GT-bbox_256x192)	AP	78.1	—	Unverified
2	MogaNet-B (384x288)	AP	77.3	—	Unverified
3	ViTPose-B (Single-task_GT-bbox_256x192)	AP	77.3	—	Unverified
4	MogaNet-S (384x288)	AP	76.4	—	Unverified
5	Bias (HRNet_256x192)	AP	75.8	—	Unverified
6	ViTPose-B (Single-task_Det-bbox_256x192)	AP	75.8	—	Unverified
7	HRNet (256x192)	AP	75.3	—	Unverified
8	MogaNet-S (256x192)	AP	74.9	—	Unverified
9	MogaNet-T (256x192)	AP	73.2	—	Unverified
10	RLE (256x192)	AP	71.3	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	Hulk(Finetune, ViT-L)	AP	37.1	—	Unverified
2	Hulk(Finetune, ViT-B)	AP	35.6	—	Unverified
3	HRFormer (HRFomer-B)	AP	34.4	—	Unverified
4	UniHCP (finetune)	AP	33.6	—	Unverified
5	HRNet (HRNet-w48 )	AP	33.5	—	Unverified
6	HRNet (HRNet-w32)	AP	32.3	—	Unverified
7	HRFormer (HRFomer-S)	AP	31.6	—	Unverified
8	SimpleBaseline (ResNet-152)	AP	29.9	—	Unverified
9	SimpleBaseline (ResNet-101)	AP	29.4	—	Unverified
10	SimpleBaseline (ResNet-50)	AP	28	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	BUCTD (PETR, with generative sampling)	APL	83.7	—	Unverified
2	OmniPose (WASPv2)	AP	79.5	—	Unverified
3	MetaPrompt-SD	AP	79	—	Unverified
4	Hulk(Finetune, ViT-L)	AP	78.7	—	Unverified
5	BUCTD (PETR, with generative sampling)	AP	77.8	—	Unverified
6	Hulk(Finetune, ViT-B)	AP	77.5	—	Unverified
7	I²R-Net (1st stage:HRFormer-B)	AP	77.3	—	Unverified
8	PATH (Partial FT)	AP	77.1	—	Unverified
9	SOLIDER (swin-B)	AP	76.6	—	Unverified
10	PEFORMER-Xcit-dino-p8	AP	72.6	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	GIM-DKM	DUC1-Acc@0.25m,10°	57.1	—	Unverified
2	GIM-LoFTR	DUC1-Acc@0.25m,10°	54.5	—	Unverified
3	GIM-SuperGlue	DUC1-Acc@0.25m,10°	53.5	—	Unverified
4	DKM	DUC1-Acc@0.25m,10°	51.5	—	Unverified
5	SuperGlue	DUC1-Acc@0.25m,10°	49	—	Unverified
6	LoFTR	DUC1-Acc@0.25m,10°	47.5	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	AdaPose	Mean mAP	93.38	—	Unverified
2	DECA-D3	Mean mAP	88.75	—	Unverified
3	V2V-PoseNet	Mean mAP	88.74	—	Unverified
4	A2J	Mean mAP	88	—	Unverified
5	REN	Mean mAP	84.9	—	Unverified
6	Multi-task learning + viewpoint invariance	Mean mAP	77.4	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	SimpleBaseline + HANet	Mean PCK@0.2	99.6	—	Unverified
2	DeciWatch	Mean PCK@0.2	99	—	Unverified
3	LSTM PM	Mean PCK@0.2	93.6	—	Unverified
4	CPM	Mean PCK@0.2	91.9	—	Unverified
5	UniTrack_i18	Mean PCK@0.2	80.5	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	4xRSN-50	PCKh@0.5	93	—	Unverified
2	Refine	PCKh@0.5	92.1	—	Unverified
3	EfficientPose IV	PCKh@0.5	91.2	—	Unverified
4	OpenPose	PCKh@0.5	88.8	—	Unverified
5	Adversarial Learning	PCKh@0.5	88.6	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	OmniPose	Mean PCK@0.2	99.4	—	Unverified
2	UniPose-LSTM	Mean PCK@0.2	99.3	—	Unverified
3	LSTM PM	Mean PCK@0.2	97.7	—	Unverified
4	Thin-Slicing	Mean PCK@0.2	96.5	—	Unverified
5	Iqbal et al.	Mean PCK@0.2	81.1	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	DP-RCNN-DeepLab (ResNet-101)	AP	68	—	Unverified