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 376–400 of 665 papers

TitleStatusHype
Learning to Estimate 3D Human Pose from Point Cloud—0
Learning Unorthogonalized Matrices for Rotation Estimation—0
Leveraging Temporal Joint Depths for Improving 3D Human Pose Estimation in Video—0
LiDARCap: Long-range Marker-less 3D Human Motion Capture with LiDAR Point Clouds—0
LiftFormer: 3D Human Pose Estimation using attention models—0
Lifting by Image -- Leveraging Image Cues for Accurate 3D Human Pose Estimation—0
Lightweight 3D Human Pose Estimation Network Training Using Teacher-Student Learning—0
Lightweight Multi-View 3D Pose Estimation through Camera-Disentangled Representation—0
LInKs "Lifting Independent Keypoints" -- Partial Pose Lifting for Occlusion Handling with Improved Accuracy in 2D-3D Human Pose Estimation—0
Listening Human Behavior: 3D Human Pose Estimation With Acoustic Signals—0
LPFormer: LiDAR Pose Estimation Transformer with Multi-Task Network—0
M3GYM: A Large-Scale Multimodal Multi-view Multi-person Pose Dataset for Fitness Activity Understanding in Real-world Settings—0
Making the Invisible Visible: Action Recognition Through Walls and Occlusions—0
Markerless Multi-view 3D Human Pose Estimation: a survey—0
Maximum-Margin Structured Learning with Deep Networks for 3D Human Pose Estimation—0
MoCap-guided Data Augmentation for 3D Pose Estimation in the Wild—0
Monocular 3D Human Pose Estimation by Predicting Depth on Joints—0
Monocular 3D Human Pose Estimation In The Wild Using Improved CNN Supervision—0
Mutual Adaptive Reasoning for Monocular 3D Multi-Person Pose Estimation—0
MV-SSM: Multi-View State Space Modeling for 3D Human Pose Estimation—0
Neural Descent for Visual 3D Human Pose and Shape—0
Non-Local Latent Relation Distillation for Self-Adaptive 3D Human Pose Estimation—0
Not All Parts Are Created Equal: 3D Pose Estimation by Modeling Bi-Directional Dependencies of Body Parts—0
Object-Occluded Human Shape and Pose Estimation From a Single Color Image—0
Occluded Human Mesh Recovery—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