3D Human Pose Estimation on Radar Dataset Participant P2 (test)
5.384P-MPJPE (0°)Engel et al.
Evaluation Results
| Method | Links | ||||
|---|---|---|---|---|---|
| Engel et al.Input Modality=Pointcloud-based2025.08 | 5.384 | 4.638 | 5.797 | 5.273 | |
| RPP-Gauss.-Gauss.-Cov.Latent Prior=Gauss., Log-likelihood=Gauss., Covariance Type=Full covariance2025.08 | 5.516 | 4.128 | 4.797 | 4.813 | |
| Ho et al.2025.08 | 6.048 | 4.54 | 5.427 | 5.339 | |
| RPP-Laplace-Gauss.Latent Prior=Laplace, Log-likelihood=Gauss.2025.08 | 6.05 | 4.25 | 5.377 | 5.226 | |
| RPP-Gauss.-Gauss.Latent Prior=Gauss., Log-likelihood=Gauss.2025.08 | 6.389 | 4.608 | 5.423 | 5.473 | |
| RPP-Normalizing-FlowsLatent Prior=Normalizing-Flows2025.08 | 7.028 | 5.503 | 6.082 | 6.204 | |
| RPP-Gauss.-Gauss.-Cov.Latent Prior=Gaussian, Log-likelihood=Gaussian, full covariance output=true2025.08 | 7.228 | 5.849 | 6.307 | 6.461 | |
| RPP-Laplace-LaplaceLatent Prior=Laplace, Log-likelihood=Laplace2025.08 | 7.396 | 5.152 | 5.938 | 6.162 | |
| RPP-Normalizing-FlowsLatent Prior=Normalizing-Flows2025.08 | 7.632 | 6.085 | 6.941 | 6.886 | |
| RPP-Laplace-Gauss.Latent Prior=Laplace, Log-likelihood=Gaussian2025.08 | 7.641 | 5.975 | 6.568 | 6.728 | |
| RPP-Gauss.-LaplaceLatent Prior=Gauss., Log-likelihood=Laplace2025.08 | 7.95 | 5.68 | 6.186 | 6.605 | |
| RPP-Gauss.-Gauss.Latent Prior=Gaussian, Log-likelihood=Gaussian2025.08 | 8.028 | 5.71 | 6.001 | 6.58 | |
| Engel et al.Pointcloud-based approach=true2025.08 | 8.318 | 6.829 | 7.503 | 7.609 | |
| Ho et al.2025.08 | 8.7 | 6.208 | 6.544 | 7.151 | |
| RPP-Laplace-LaplaceLatent Prior=Laplace, Log-likelihood=Laplace2025.08 | 9.061 | 6.665 | 6.949 | 7.558 | |
| RPP-Gauss.-LaplaceLatent Prior=Gaussian, Log-likelihood=Laplace2025.08 | 9.491 | 7.015 | 7.163 | 7.89 | |
| Evidential Regression2025.08 | 12.13 | 11.718 | 12.118 | 11.989 | |
| Evidential Regression2025.08 | 12.732 | 11.396 | 11.562 | 11.897 |