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 351–375 of 665 papers

TitleStatusHype
HybridCap: Inertia-aid Monocular Capture of Challenging Human Motions—0
Image-based Synthesis for Deep 3D Human Pose Estimation—0
Implicit 3D Human Mesh Recovery using Consistency with Pose and Shape from Unseen-view—0
Improving the Robustness of 3D Human Pose Estimation: A Benchmark and Learning from Noisy Input—0
Inference Stage Optimization for Cross-scenario 3D Human Pose Estimation—0
Integrated In-vehicle Monitoring System Using 3D Human Pose Estimation and Seat Belt Segmentation—0
Intelligent Carpet: Inferring 3D Human Pose From Tactile Signals—0
In the Wild Human Pose Estimation Using Explicit 2D Features and Intermediate 3D Representations—0
Inverse Graphics with Probabilistic CAD Models—0
Iterated Second-Order Label Sensitive Pooling for 3D Human Pose Estimation—0
It's all Relative: Monocular 3D Human Pose Estimation from Weakly Supervised Data—0
IVT: An End-to-End Instance-guided Video Transformer for 3D Pose Estimation—0
KAMA: 3D Keypoint Aware Body Mesh Articulation—0
Kinematic-Structure-Preserved Representation for Unsupervised 3D Human Pose Estimation—0
KinePose: A temporally optimized inverse kinematics technique for 6DOF human pose estimation with biomechanical constraints—0
LCR-Net: Localization-Classification-Regression for Human Pose—0
LCR-Net++: Multi-person 2D and 3D Pose Detection in Natural Images—0
Learnable human mesh triangulation for 3D human pose and shape estimation—0
Learning Dynamical Human-Joint Affinity for 3D Pose Estimation in Videos—0
Learning Local Recurrent Models for Human Mesh Recovery—0
Learning Monocular 3D Human Pose Estimation from Multi-view Images—0
Learning Pose Grammar for Monocular 3D Pose Estimation—0
Learning Pose Grammar to Encode Human Body Configuration for 3D Pose Estimation—0
Learning Probabilistic Non-Linear Latent Variable Models for Tracking Complex Activities—0
Learning to Estimate 3D Human Pose and Shape from a Single Color Image—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