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 551–600 of 665 papers

TitleStatusHype
On Boosting Single-Frame 3D Human Pose Estimation via Monocular Videos—0
Online Test-time Adaptation for 3D Human Pose Estimation: A Practical Perspective with Estimated 2D Poses—0
On the role of depth predictions for 3D human pose estimation—0
On Triangulation as a Form of Self-Supervision for 3D Human Pose Estimation—0
Optimising 2D Pose Representation: Improve Accuracy, Stability and Generalisability Within Unsupervised 2D-3D Human Pose Estimation—0
Optimizing Network Structure for 3D Human Pose Estimation—0
PandaNet: Anchor-Based Single-Shot Multi-Person 3D Pose Estimation—0
PandaNet : Anchor-Based Single-Shot Multi-Person 3D Pose Estimation—0
Patch-based 3D Human Pose Refinement—0
PC-HMR: Pose Calibration for 3D Human Mesh Recovery from 2D Images/Videos—0
Picture: A Probabilistic Programming Language for Scene Perception—0
LiCamPose: Combining Multi-View LiDAR and RGB Cameras for Robust Single-frame 3D Human Pose Estimation—0
PONet: Robust 3D Human Pose Estimation via Learning Orientations Only—0
Pose-Conditioned Joint Angle Limits for 3D Human Pose Reconstruction—0
PoseGraphNet++: Enriching 3D Human Pose with Orientation Estimation—0
PoseGU: 3D Human Pose Estimation with Novel Human Pose Generator and Unbiased Learning—0
PoseIRM: Enhance 3D Human Pose Estimation on Unseen Camera Settings via Invariant Risk Minimization—0
Pose-Oriented Transformer with Uncertainty-Guided Refinement for 2D-to-3D Human Pose Estimation—0
Pose Priors from Language Models—0
PoseRN: A 2D pose refinement network for bias-free multi-view 3D human pose estimation—0
PostoMETRO: Pose Token Enhanced Mesh Transformer for Robust 3D Human Mesh Recovery—0
Predicting Camera Viewpoint Improves Cross-dataset Generalization for 3D Human Pose Estimation—0
Predicting people’s 3D poses from short sequences—0
Proactive Multi-Camera Collaboration For 3D Human Pose Estimation—0
Probabilistic 3D Human Shape and Pose Estimation from Multiple Unconstrained Images in the Wild—0
Propagating LSTM: 3D Pose Estimation based on Joint Interdependency—0
ProPLIKS: Probablistic 3D human body pose estimation—0
PSVT: End-to-End Multi-person 3D Pose and Shape Estimation with Progressive Video Transformers—0
Random Tree Walk Toward Instantaneous 3D Human Pose Estimation—0
Real-Time Hybrid Mapping of Populated Indoor Scenes using a Low-Cost Monocular UAV—0
Real-time, low-cost multi-person 3D pose estimation—0
ProxyCap: Real-time Monocular Full-body Capture in World Space via Human-Centric Proxy-to-Motion Learning—0
Recent Advances in Monocular 2D and 3D Human Pose Estimation: A Deep Learning Perspective—0
Recurrent 3D Pose Sequence Machines—0
RemoCap: Disentangled Representation Learning for Motion Capture—0
ReMP: Reusable Motion Prior for Multi-domain 3D Human Pose Estimation and Motion Inbetweening—0
Representation learning of vertex heatmaps for 3D human mesh reconstruction from multi-view images—0
RFPose-OT: RF-Based 3D Human Pose Estimation via Optimal Transport Theory—0
Robust Estimation of 3D Human Poses from a Single Image—0
RSB-Pose: Robust Short-Baseline Binocular 3D Human Pose Estimation with Occlusion Handling—0
RTMW: Real-Time Multi-Person 2D and 3D Whole-body Pose Estimation—0
RT-Pose: A 4D Radar Tensor-based 3D Human Pose Estimation and Localization Benchmark—0
Self-Attentive 3D Human Pose and Shape Estimation from Videos—0
Self-learning Canonical Space for Multi-view 3D Human Pose Estimation—0
Self-supervised 3D Human Pose Estimation from a Single Image—0
Self-Supervised 3D Human Pose Estimation in Static Video Via Neural Rendering—0
Self-Supervised 3D Human Pose Estimation via Part Guided Novel Image Synthesis—0
Self-Supervised Multi-View Synchronization Learning for 3D Pose Estimation—0
Semantic Estimation of 3D Body Shape and Pose using Minimal Cameras—0
Semantics-aware Test-time Adaptation 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