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

Showing 351–375 of 665 papers

TitleStatusHype
Sequential 3D Human Pose Estimation Using Adaptive Point Cloud Sampling StrategyCode1
DECA: Deep viewpoint-Equivariant human pose estimation using Capsule AutoencodersCode1
Self-Supervised 3D Human Pose Estimation with Multiple-View GeometryCode0
Learning Skeletal Graph Neural Networks for Hard 3D Pose EstimationCode0
FrankMocap: A Monocular 3D Whole-Body Pose Estimation System via Regression and IntegrationCode2
MetaPose: Fast 3D Pose from Multiple Views without 3D SupervisionCode0
VoxelTrack: Multi-Person 3D Human Pose Estimation and Tracking in the Wild—0
LASOR: Learning Accurate 3D Human Pose and Shape Via Synthetic Occlusion-Aware Data and Neural Mesh RenderingCode1
Probabilistic Monocular 3D Human Pose Estimation with Normalizing FlowsCode1
Improving Robustness and Accuracy via Relative Information Encoding in 3D Human Pose EstimationCode1
Learning Local Recurrent Models for Human Mesh Recovery—0
Human Pose Regression with Residual Log-likelihood EstimationCode1
Conditional Directed Graph Convolution for 3D Human Pose EstimationCode1
Everybody Is Unique: Towards Unbiased Human Mesh Recovery—0
PoseRN: A 2D pose refinement network for bias-free multi-view 3D human pose estimation—0
Real-Time Multi-View 3D Human Pose Estimation using Semantic Feedback to Smart Edge SensorsCode1
Motion Projection Consistency Based 3D Human Pose Estimation with Virtual Bones from Monocular Videos—0
Part-Aware Measurement for Robust Multi-View Multi-Human 3D Pose Estimation and TrackingCode1
Intelligent Carpet: Inferring 3D Human Pose From Tactile Signals—0
THUNDR: Transformer-based 3D HUmaN Reconstruction with Markers—0
TesseTrack: End-to-End Learnable Multi-Person Articulated 3D Pose Tracking—0
SVMAC: Unsupervised 3D Human Pose Estimation from a Single Image with Single-view-multi-angle Consistency—0
3D Human Pose Estimation Based on 2D-3D Consistency with Synchronized Adversarial Training—0
Adapted Human Pose: Monocular 3D Human Pose Estimation with Zero Real 3D Pose DataCode0
Weakly-supervised 3D Human Pose Estimation with Cross-view U-shaped Graph Convolutional Network—0
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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