3D Human Pose Estimation on Radar Dataset Participant P12 (test)
5.122P-MPJPE (0°)Engel et al.
Evaluation Results
| Method | Links | ||||
|---|---|---|---|---|---|
| Engel et al.Input Modality=Pointcloud-based2025.08 | 5.122 | 4.132 | 5.156 | 4.803 | |
| RPP-Gauss.-Gauss.-Cov.Latent Prior=Gauss., Log-likelihood=Gauss., Covariance Type=Full covariance2025.08 | 5.363 | 3.738 | 4.654 | 4.585 | |
| RPP-Laplace-Gauss.Latent Prior=Laplace, Log-likelihood=Gauss.2025.08 | 5.772 | 3.98 | 4.865 | 4.872 | |
| Ho et al.2025.08 | 5.939 | 4.249 | 5.097 | 5.095 | |
| RPP-Gauss.-Gauss.Latent Prior=Gauss., Log-likelihood=Gauss.2025.08 | 5.939 | 3.948 | 4.9 | 4.929 | |
| RPP-Normalizing-FlowsLatent Prior=Normalizing-Flows2025.08 | 6.264 | 4.363 | 5.368 | 5.665 | |
| RPP-Laplace-LaplaceLatent Prior=Laplace, Log-likelihood=Laplace2025.08 | 6.601 | 4.374 | 4.87 | 5.282 | |
| RPP-Gauss.-LaplaceLatent Prior=Gauss., Log-likelihood=Laplace2025.08 | 6.927 | 4.692 | 5.238 | 5.619 | |
| RPP-Normalizing-FlowsLatent Prior=Normalizing-Flows2025.08 | 7.417 | 5.788 | 6.639 | 6.947 | |
| RPP-Laplace-Gauss.Latent Prior=Laplace, Log-likelihood=Gaussian2025.08 | 9.063 | 5.202 | 5.763 | 6.676 | |
| RPP-Gauss.-Gauss.-Cov.Latent Prior=Gaussian, Log-likelihood=Gaussian, full covariance output=true2025.08 | 9.369 | 4.922 | 5.635 | 6.642 | |
| RPP-Gauss.-Gauss.Latent Prior=Gaussian, Log-likelihood=Gaussian2025.08 | 9.97 | 5.556 | 5.581 | 7.036 | |
| RPP-Laplace-LaplaceLatent Prior=Laplace, Log-likelihood=Laplace2025.08 | 9.988 | 5.591 | 5.683 | 7.087 | |
| Evidential Regression2025.08 | 10.417 | 9.346 | 9.989 | 9.917 | |
| RPP-Gauss.-LaplaceLatent Prior=Gaussian, Log-likelihood=Laplace2025.08 | 10.777 | 5.813 | 6.007 | 7.532 | |
| Ho et al.2025.08 | 10.881 | 6.476 | 6.481 | 7.946 | |
| Engel et al.Pointcloud-based approach=true2025.08 | 11.527 | 6.108 | 6.733 | 8.186 | |
| Evidential Regression2025.08 | 13.522 | 9.632 | 9.923 | 11.026 |