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 28512900 of 4228 papers

TitleStatusHype
Self-Avatar Animation in Virtual Reality: Impact of Motion Signals Artifacts on the Full-Body Pose Reconstruction0
Self-Constrained Inference Optimization on Structural Groups for Human Pose Estimation0
Self-Correctable and Adaptable Inference for Generalizable Human Pose Estimation0
Self-Improving Visual Odometry0
Self-learning Canonical Space for Multi-view 3D Human Pose Estimation0
Self-Paced Deep Regression Forests with Consideration on Underrepresented Examples0
SelfSplat: Pose-Free and 3D Prior-Free Generalizable 3D Gaussian Splatting0
Self-Supervised 3D Hand Pose Estimation Through Training by Fitting0
Self-supervised 3D Human Pose Estimation from a Single Image0
Self-Supervised 3D Human Pose Estimation in Static Video Via Neural Rendering0
Self-Supervised 3D Human Pose Estimation via Part Guided Novel Image Synthesis0
Self-supervised Keypoint Correspondences for Multi-Person Pose Estimation and Tracking in Videos0
Self-supervised Learning of 3D Object Understanding by Data Association and Landmark Estimation for Image Sequence0
Self-Supervised Learning of Motion-Informed Latents0
Self-supervised Learning of Occlusion Aware Flow Guided 3D Geometry Perception with Adaptive Cross Weighted Loss from Monocular Videos0
Self-supervised Learning of Pose Embeddings from Spatiotemporal Relations in Videos0
Self-supervised Monocular Depth and Pose Estimation for Endoscopy with Generative Latent Priors0
Self-Supervised Monocular Visual Drone Model Identification through Improved Occlusion Handling0
Self-Supervised Multi-View Synchronization Learning for 3D Pose Estimation0
Self-supervised Optimization of Hand Pose Estimation using Anatomical Features and Iterative Learning0
Self-supervised pretraining of vision transformers for animal behavioral analysis and neural encoding0
Self-Supervision and Spatial-Sequential Attention Based Loss for Multi-Person Pose Estimation0
SelfVIO: Self-Supervised Deep Monocular Visual-Inertial Odometry and Depth Estimation0
Semantic Alignment of LiDAR Data at City Scale0
Semantically Video Coding: Instill Static-Dynamic Clues into Structured Bitstream for AI Tasks0
Semantic Estimation of 3D Body Shape and Pose using Minimal Cameras0
Semantic Part Segmentation using Compositional Model combining Shape and Appearance0
Semantics-aware Test-time Adaptation for 3D Human Pose Estimation0
Semi- and Weakly-supervised Human Pose Estimation0
SemiHand: Semi-Supervised Hand Pose Estimation With Consistency0
SemiMultiPose: A Semi-supervised Multi-animal Pose Estimation Framework0
Semi-Perspective Decoupled Heatmaps for 3D Robot Pose Estimation from Depth Maps0
Semi-supervised 2D Human Pose Estimation via Adaptive Keypoint Masking0
Semi-supervised 3D Hand-Object Pose Estimation via Pose Dictionary Learning0
Semi-Supervised 3D Hand Shape and Pose Estimation with Label Propagation0
Semi-supervised Pose Estimation with Geometric Latent Representations0
SEMPose: A Single End-to-end Network for Multi-object Pose Estimation0
Sensor Fusion of Camera, GPS and IMU using Fuzzy Adaptive Multiple Motion Models0
Separated Attention: An Improved Cycle GAN Based Under Water Image Enhancement Method0
SEPose: A Synthetic Event-based Human Pose Estimation Dataset for Pedestrian Monitoring0
SeqHAND:RGB-Sequence-Based 3D Hand Pose and Shape Estimation0
SeqHAND: RGB-Sequence-Based 3D Hand Pose and Shape Estimation0
SGPA: Structure-Guided Prior Adaptation for Category-Level 6D Object Pose Estimation0
Shakes on a Plane: Unsupervised Depth Estimation from Unstabilized Photography0
Shape-aware Multi-Person Pose Estimation from Multi-View Images0
Shape-based pose estimation for automatic standard views of the knee0
ShapeGraFormer: GraFormer-Based Network for Hand-Object Reconstruction from a Single Depth Map0
SHAPE: Linear-Time Camera Pose Estimation With Quadratic Error-Decay0
ShapeShift: Superquadric-based Object Pose Estimation for Robotic Grasping0
ShaRPy: Shape Reconstruction and Hand Pose Estimation from RGB-D with Uncertainty0
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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