Face Alignment On 300W
Métriques
NME_inter-ocular (%, Challenge)
NME_inter-ocular (%, Common)
NME_inter-ocular (%, Full)
NME_inter-pupil (%, Challenge)
NME_inter-pupil (%, Common)
NME_inter-pupil (%, Full)
Résultats
Résultats de performance de divers modèles sur ce benchmark
Tableau comparatif
| Nom du modèle | NME_inter-ocular (%, Challenge) | NME_inter-ocular (%, Common) | NME_inter-ocular (%, Full) | NME_inter-pupil (%, Challenge) | NME_inter-pupil (%, Common) | NME_inter-pupil (%, Full) |
|---|---|---|---|---|---|---|
| adaptive-wing-loss-for-robust-face-alignment | 4.52 | 2.72 | 3.07 | 6.52 | 3.77 | 4.31 |
| propagationnet-propagate-points-to-curve-to-1 | 3.99 | 2.67 | 2.93 | 5.75 | 3.7 | 4.1 |
| sparse-local-patch-transformer-for-robust | 4.90 | 2.75 | 3.17 | - | - | - |
| efficient-and-accurate-face-alignment-by | 4.78 | 2.71 | 3.12 | 6.89 | 3.76 | 4.37 |
| lddmm-face-large-deformation-diffeomorphic | 5.4 | 3.07 | 3.53 | - | - | - |
| deep-alignment-network-a-convolutional-neural | 4.88 | 3.09 | 3.44 | 7.05 | 4.29 | 4.83 |
| robust-facial-landmark-detection-via | 6.67 | 3.56 | 4.17 | - | - | - |
| aggregation-via-separation-boosting-facial | 6.49 | 3.21 | 3.86 | - | - | - |
| atf-towards-robust-face-alignment-via | 4.89 | 2.75 | 3.17 | - | - | - |
| cascade-of-encoder-decoder-cnns-with-learned | 5.15 | 2.85 | 3.3 | 7.44 | 3.96 | 4.64 |
| hih-towards-more-accurate-face-alignment-via | 4.89 | 2.65 | 3.09 | - | - | - |
| wing-loss-for-robust-facial-landmark | - | - | - | 7.18 | 3.27 | 4.04 |
| acr-loss-adaptive-coordinate-based-regression | 5.36 | 3.36 | 3.75 | - | - | - |
| style-aggregated-network-for-facial-landmark | 6.60 | 3.34 | 3.98 | - | - | - |
| 3d-face-reconstruction-with-dense-landmarks | 4.8 | 3.03 | - | - | - | - |
| general-facial-representation-learning-in-a | 4.45 | 2.56 | 2.93 | 6.42 | 3.53 | 4.11 |
| deep-active-shape-model-for-face-alignment | 7.35 | 3.88 | 4.59 | - | - | - |
| 190807919 | 5.15 | 2.87 | 3.32 | - | - | - |
| 3fabrec-fast-few-shot-face-alignment-by | 5.74 | 3.36 | 3.82 | - | - | - |
| fast-and-accurate-structure-coherence | 4.93 | 2.88 | 3.28 | - | - | - |
| fake-it-till-you-make-it-face-analysis-in-the | 4.86 | 3.09 | - | - | - | - |
| learning-robust-facial-landmark-detection-via | 5.03 | 2.85 | 3.28 | - | - | - |
| pixel-in-pixel-net-towards-efficient-facial | 4.89 | 2.78 | 3.19 | - | - | - |
| deep-active-shape-model-for-face-alignment | 8.2 | 4.82 | 5.50 | - | - | - |
| laplace-landmark-localization | 7.01 | 3.28 | 4.01 | - | - | - |
| fiducial-focus-augmentation-for-facial | 4.47 | 2.51 | 2.89 | - | - | - |
| subpixel-heatmap-regression-for-facial | 4.13 | 2.61 | 2.94 | - | - | - |
| towards-accurate-facial-landmark-detection-1 | 4.48 | 2.6 | 2.96 | - | - | - |
| improving-landmark-localization-with-semi | 7.78 | 4.20 | 4.90 | - | - | - |
| adnet-leveraging-error-bias-towards-normal | 4.58 | 2.53 | 2.93 | 6.47 | 3.51 | 4.08 |
| semantic-alignment-finding-semantically | - | - | - | 6.38 | 3.45 | 4.02 |
| facial-landmarks-detection-by-self-iterative | - | - | - | 8.14 | 4.29 | 5.04 |
| shape-preserving-facial-landmarks-with-graph | 4.66 | 2.59 | 2.99 | 6.73 | 3.59 | 4.20 |
| face-alignment-in-full-pose-range-a-3d-total | 8.07 | 5.09 | 5.63 | 10.59 | 6.15 | 7.01 |
| when-liebig-s-barrel-meets-facial-landmark | 4.6 | 2.73 | 3.09 | - | - | - |
| facial-landmark-points-detection-using | 6.13 | 3.56 | 4.06 | - | - | - |
| look-at-boundary-a-boundary-aware-face | 5.19 | 2.98 | 3.49 | 6.98 | 3.42 | 4.12 |
| deep-structured-prediction-for-facial-1 | 4.84 | 2.93 | 3.30 | 6.98 | 4.06 | 4.63 |
| face-alignment-using-a-3d-deeply-initialized | 4.92 | 2.69 | 3.13 | 7.10 | 3.73 | 4.39 |
| general-facial-representation-learning-in-a | 4.42 | 2.50 | 2.88 | 6.38 | 3.46 | 4.05 |
| exploring-stylegan-latent-space-for-face | 5.30 | 2.97 | 3.42 | - | - | - |
| scaf-skip-connections-in-auto-encoder-for | 5.83 | 3.48 | 3.95 | - | - | - |
| freeenricher-enriching-face-landmarks-without | - | - | 2.87 | - | - | - |
| anchorface-an-anchor-based-facial-landmark | 6.19 | 3.12 | 3.72 | - | - | - |
| high-resolution-representations-for-labeling | 5.15 | 2.87 | 3.32 | - | - | - |
| a-deeply-initialized-coarse-to-fine-ensemble | 5.22 | 2.76 | 3.24 | 7.54 | 3.83 | 4.55 |
| star-loss-reducing-semantic-ambiguity-in-1 | 4.32 | 2.52 | 2.87 | 6.22 | 3.5 | 4.03 |
| decafa-deep-convolutional-cascade-for-face | 5.26 | 2.93 | 3.39 | - | - | - |