Multi Hypotheses 3D Human Pose Estimation On
Metrics
Average MPJPE (mm)
Average PMPJPE (mm)
Using 2D ground-truth joints
Results
Performance results of various models on this benchmark
| Paper Title | ||||
|---|---|---|---|---|
| Li et al. | 73.9 | 44.3 | - | Weakly Supervised Generative Network for Multiple 3D Human Pose Hypotheses |
| cGNF w Lsample | 53 | - | - | Multi-hypothesis 3D human pose estimation metrics favor miscalibrated distributions |
| MDN | 52.7 | 42.6 | - | Generating Multiple Hypotheses for 3D Human Pose Estimation with Mixture Density Network |
| cGNF xlarge w Lsample | 48.5 | - | - | Multi-hypothesis 3D human pose estimation metrics favor miscalibrated distributions |
| Sharma et al. | 46.8 | 37.3 | - | Monocular 3D Human Pose Estimation by Generation and Ordinal Ranking |
| GraphMDN | 46.2 | 36.3 | - | GraphMDN: Leveraging graph structure and deep learning to solve inverse problems |
| GFPose (HPJ2D-000, S=200) | 35.6 | 30.5 | 16.9 | GFPose: Learning 3D Human Pose Prior with Gradient Fields |
| D3DP | 35.4 | - | No | Diffusion-Based 3D Human Pose Estimation with Multi-Hypothesis Aggregation |
| GFPose (HPJ2D-010, S=200) | 35.1 | - | - | GFPose: Learning 3D Human Pose Prior with Gradient Fields |
| MHEntropy | - | 36.8 | - | MHEntropy: Entropy Meets Multiple Hypotheses for Pose and Shape Recovery |
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