3D Multi Person Human Pose Estimation On
Métriques
3DPCK
Résultats
Résultats de performance de divers modèles sur ce benchmark
Nom du modèle | 3DPCK | Paper Title | Repository |
|---|---|---|---|
| Unsupervised Cross-Modal Alignment | 78.4 | Unsupervised Cross-Modal Alignment for Multi-Person 3D Pose Estimation | - |
| 3DCrowdNet (HigherHRNet) | 72.7 | Learning to Estimate Robust 3D Human Mesh from In-the-Wild Crowded Scenes | - |
| Temporal Smoothing | 85.3 | Temporal Smoothing for 3D Human Pose Estimation and Localization for Occluded People | - |
| Multi-HMR | 89.5 | Multi-HMR: Multi-Person Whole-Body Human Mesh Recovery in a Single Shot | - |
| BMP | 73.83 | Body Meshes as Points | - |
| HG-RCNN | 74.2 | Multi-Person 3D Human Pose Estimation from Monocular Images | - |
| 3DCrowdNet | 76.2 | Three Recipes for Better 3D Pseudo-GTs of 3D Human Mesh Estimation in the Wild | - |
| Multi-Person 3D Pose and Shape Estimation via Inverse Kinematics and Refinement | 89.9 | Multi-Person 3D Pose and Shape Estimation via Inverse Kinematics and Refinement | - |
| MUG | 76.27 | MUG: Multi-human Graph Network for 3D Mesh Reconstruction from 2D Pose | - |
| SelecSLS | 75.8 | XNect: Real-time Multi-Person 3D Motion Capture with a Single RGB Camera | - |
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