SOTAVerified

3D Human Pose Estimation

3D Human Pose Estimation is a computer vision task that involves estimating the 3D positions and orientations of body joints and bones from 2D images or videos. The goal is to reconstruct the 3D pose of a person in real-time, which can be used in a variety of applications, such as virtual reality, human-computer interaction, and motion analysis.

Papers

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Showing 601–650 of 665 papers

TitleStatusHype
LCR-Net++: Multi-person 2D and 3D Pose Detection in Natural Images—0
2D/3D Pose Estimation and Action Recognition using Multitask Deep LearningCode0
Image-based Synthesis for Deep 3D Human Pose Estimation—0
DensePose: Dense Human Pose Estimation In The WildCode0
End-to-end Recovery of Human Shape and PoseCode0
Single-Shot Multi-Person 3D Pose Estimation From Monocular RGBCode0
Self-supervised Learning of Motion CaptureCode0
DeepSkeleton: Skeleton Map for 3D Human Pose Regression—0
Learning 3D Human Pose from Structure and MotionCode0
Exploiting temporal information for 3D pose estimationCode0
Integral Human Pose RegressionCode0
V2V-PoseNet: Voxel-to-Voxel Prediction Network for Accurate 3D Hand and Human Pose Estimation from a Single Depth MapCode0
Learning Pose Grammar to Encode Human Body Configuration for 3D Pose Estimation—0
Monocular 3D Human Pose Estimation by Predicting Depth on Joints—0
Total capture: 3D human pose estimation fusing video and inertial sensors—0
Recurrent 3D Pose Sequence Machines—0
LCR-Net: Localization-Classification-Regression for Human Pose—0
Holistic Planimetric prediction to Local Volumetric prediction for 3D Human Pose Estimation—0
Adversarial Inverse Graphics Networks: Learning 2D-to-3D Lifting and Image-to-Image Translation from Unpaired Supervision—0
A Dual-Source Approach for 3D Human Pose Estimation from a Single Image—0
VNect: Real-time 3D Human Pose Estimation with a Single RGB CameraCode0
Harvesting Multiple Views for Marker-less 3D Human Pose Annotations—0
2D-3D Pose Consistency-based Conditional Random Fields for 3D Human Pose Estimation—0
Towards 3D Human Pose Estimation in the Wild: a Weakly-supervised ApproachCode0
Compositional Human Pose RegressionCode0
Sparse Inertial Poser: Automatic 3D Human Pose Estimation from Sparse IMUs—0
Generating Multiple Diverse Hypotheses for Human 3D Pose Consistent with 2D Joint Detections—0
Deep Multitask Architecture for Integrated 2D and 3D Human Sensing—0
A Multi-view RGB-D Approach for Human Pose Estimation in Operating RoomsCode0
Unite the People: Closing the Loop Between 3D and 2D Human RepresentationsCode0
Learning from Synthetic HumansCode0
Lifting from the Deep: Convolutional 3D Pose Estimation from a Single ImageCode0
3D Human Pose Estimation = 2D Pose Estimation + Matching—0
Monocular 3D Human Pose Estimation In The Wild Using Improved CNN Supervision—0
3D Human Pose Estimation from a Single Image via Distance Matrix Regression—0
Learning to Fuse 2D and 3D Image Cues for Monocular Body Pose EstimationCode0
Deep Kinematic Pose Regression—0
Human Pose Estimation in Space and Time using 3D CNN—0
3D Human Pose Estimation Using Convolutional Neural Networks with 2D Pose Information—0
MoCap-guided Data Augmentation for 3D Pose Estimation in the Wild—0
Structured Prediction of 3D Human Pose with Deep Neural Networks—0
Fusing Audio, Textual and Visual Features for Sentiment Analysis of News Videos—0
Towards Viewpoint Invariant 3D Human Pose EstimationCode0
Convolutional Pose MachinesCode0
Understanding Human-Centric Images: From Geometry to Fashion—0
Sparseness Meets Deepness: 3D Human Pose Estimation from Monocular VideoCode0
Direct Prediction of 3D Body Poses from Motion Compensated Sequences—0
A Dual-Source Approach for 3D Pose Estimation from a Single Image—0
Sparse Representation for 3D Shape Estimation: A Convex Relaxation Approach—0
Maximum-Margin Structured Learning with Deep Networks for 3D Human Pose Estimation—0
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All datasets3DPWMPI-INF-3DHPHuman3.6MHumanEva-IH3WBTotal CaptureEMDBAGORAPanopticSurreal3D Poses in the Wild ChallengeAIST++

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Simple-baselinePA-MPJPE157—Unverified
2HMRMPJPE130—Unverified
3BMPMPVPE119.3—Unverified
4SPINMPVPE116.4—Unverified
5Wenshuo et a;.MPVPE112.6—Unverified
6TCMR (T=16 w/o 3DPW)MPVPE111.5—Unverified
7CHOMPMPVPE110.1—Unverified
8PC-HMRMPVPE108.6—Unverified
93DCrowdNetMPVPE108.5—Unverified
10SMPLifyPA-MPJPE106.8—Unverified
#ModelMetricClaimedVerifiedStatus
1VNect (Augm.)MPJPE124.7—Unverified
2HMRMPJPE124.2—Unverified
3Single-Shot Multi-PersonMPJPE122.2—Unverified
4MehtaMPJPE117.6—Unverified
5PONetMPJPE115—Unverified
6Pose Consensus (monocular)MPJPE112.1—Unverified
7GeoRep (fully-supervised)MPJPE110.8—Unverified
8XFormer (HRNet)MPJPE109.8—Unverified
9EpipolarPose (fully-supervised)MPJPE108.99—Unverified
10SPINMPJPE105.2—Unverified