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 1–10 of 665 papers

TitleStatusHype
Systematic Comparison of Projection Methods for Monocular 3D Human Pose Estimation on Fisheye Images—0
ExtPose: Robust and Coherent Pose Estimation by Extending ViTs—0
PoseGRAF: Geometric-Reinforced Adaptive Fusion for Monocular 3D Human Pose EstimationCode0
Learning Pyramid-structured Long-range Dependencies for 3D Human Pose EstimationCode0
UPTor: Unified 3D Human Pose Dynamics and Trajectory Prediction for Human-Robot Interaction—0
PoseBench3D: A Cross-Dataset Analysis Framework for 3D Human Pose EstimationCode1
HDiffTG: A Lightweight Hybrid Diffusion-Transformer-GCN Architecture for 3D Human Pose EstimationCode0
Continuous Normalizing Flows for Uncertainty-Aware Human Pose Estimation—0
3D Human Pose Estimation via Spatial Graph Order Attention and Temporal Body Aware TransformerCode0
Unsupervised Cross-Domain 3D Human Pose Estimation via Pseudo-Label-Guided Global Transforms—0
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All datasets3DPWMPI-INF-3DHPHuman3.6MHumanEva-IH3WBTotal CaptureEMDBAGORAPanopticSurreal3D Poses in the Wild ChallengeAIST++

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1self-supervised mocapMPJPE64.4—Unverified
2PC-HMRMPJPE51.7—Unverified
3BodyNetMPJPE49.1—Unverified
4Cross Dataset GeneralizationMPJPE37.1—Unverified
5VirtualMarkerMPJPE36.9—Unverified
6DynaBOAPA-MPJPE34—Unverified