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

Showing 426450 of 665 papers

TitleStatusHype
Error Bounds of Projection Models in Weakly Supervised 3D Human Pose Estimation0
HPERL: 3D Human Pose Estimation from RGB and LiDARCode0
Multi-Scale Networks for 3D Human Pose Estimation with Inference Stage Optimization0
A review of 3D human pose estimation algorithms for markerless motion capture0
Self-Supervised Multi-View Synchronization Learning for 3D Pose Estimation0
Synthetic Training for Accurate 3D Human Pose and Shape Estimation in the WildCode1
Beyond Weak Perspective for Monocular 3D Human Pose Estimation0
SSP-Net: Scalable Sequential Pyramid Networks for Real-Time 3D Human Pose Regression0
LiftFormer: 3D Human Pose Estimation using attention models0
Monocular, One-stage, Regression of Multiple 3D PeopleCode2
SMAP: Single-Shot Multi-Person Absolute 3D Pose EstimationCode1
Pose2Mesh: Graph Convolutional Network for 3D Human Pose and Mesh Recovery from a 2D Human PoseCode1
Monocular Expressive Body Regression through Body-Driven AttentionCode1
Human Body Model Fitting by Learned Gradient Descent0
Neural Descent for Visual 3D Human Pose and Shape0
Weakly Supervised Generative Network for Multiple 3D Human Pose HypothesesCode1
3D Human Motion Estimation via Motion Compression and RefinementCode1
I2L-MeshNet: Image-to-Lixel Prediction Network for Accurate 3D Human Pose and Mesh Estimation from a Single RGB ImageCode1
Unsupervised Cross-Modal Alignment for Multi-Person 3D Pose EstimationCode0
End-to-end Dynamic Matching Network for Multi-view Multi-person 3d Pose Estimation0
A Comprehensive Study of Weight Sharing in Graph Networks for 3D Human Pose EstimationCode0
Towards Part-aware Monocular 3D Human Pose Estimation: An Architecture Search Approach0
HMOR: Hierarchical Multi-Person Ordinal Relations for Monocular Multi-Person 3D Pose Estimation0
Perceiving 3D Human-Object Spatial Arrangements from a Single Image in the WildCode1
3D Human Shape and Pose from a Single Low-Resolution Image with Self-Supervised LearningCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Simple-baselinePA-MPJPE157Unverified
2HMRMPJPE130Unverified
3BMPMPVPE119.3Unverified
4SPINMPVPE116.4Unverified
5Wenshuo et a;.MPVPE112.6Unverified
6TCMR (T=16 w/o 3DPW)MPVPE111.5Unverified
7CHOMPMPVPE110.1Unverified
8PC-HMRMPVPE108.6Unverified
93DCrowdNetMPVPE108.5Unverified
10SMPLifyPA-MPJPE106.8Unverified
#ModelMetricClaimedVerifiedStatus
1VNect (Augm.)MPJPE124.7Unverified
2HMRMPJPE124.2Unverified
3Single-Shot Multi-PersonMPJPE122.2Unverified
4MehtaMPJPE117.6Unverified
5PONetMPJPE115Unverified
6Pose Consensus (monocular)MPJPE112.1Unverified
7GeoRep (fully-supervised)MPJPE110.8Unverified
8XFormer (HRNet)MPJPE109.8Unverified
9EpipolarPose (fully-supervised)MPJPE108.99Unverified
10SPINMPJPE105.2Unverified