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 401–425 of 665 papers

TitleStatusHype
Occluded Joints Recovery in 3D Human Pose Estimation based on Distance Matrix—0
Occlusion-Aware Networks for 3D Human Pose Estimation in Video—0
Occlusion Handling in 3D Human Pose Estimation with Perturbed Positional Encoding—0
Occlusion Resilient 3D Human Pose Estimation—0
Occlusion Robust 3D Human Pose Estimation with StridedPoseGraphFormer and Data Augmentation—0
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
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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