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Models/FASE

FASE

Reported on 40 benchmarks across 5 tasks

Note: results are matched by exact model name. Different papers may use the same name for different model variants.

Computer Vision16 results

  • Face Reconstructionon300W
    NME_inter-ocular (%, Challenge)
    5.3
    best: 8.2 (ASMNet)
  • Face Reconstructionon300W
    NME_inter-ocular (%, Common)
    2.97
    best: 5.09 (3DDFA)
  • Face Reconstructionon300W
    NME_inter-ocular (%, Full)
    3.42
    best: 5.63 (3DDFA)
  • Face ReconstructiononAFLW-19
    AUC_box@0.07 (%, Full)
    79.1
    best: 81.8 (FiFA)
  • Face ReconstructiononAFLW-19
    NME_box (%, Full)
    1.45
    best: 4.04 (SAN)
  • Face ReconstructiononAFLW-19
    NME_diag (%, Frontal)
    0.9
    best: 2.68 (CFSS)
  • Face ReconstructiononAFLW-19
    NME_diag (%, Full)
    1.02
    best: 3.92 (CFSS)
  • Face ReconstructiononWFLW
    NME (inter-ocular)
    4.62
    best: 11.13 (ESR)
  • 3D Face ReconstructiononAFLW-19
    AUC_box@0.07 (%, Full)
    79.1
    best: 81.8 (FiFA)
  • 3D Face ReconstructiononAFLW-19
    NME_box (%, Full)
    1.45
    best: 4.04 (SAN)
  • 3D Face ReconstructiononAFLW-19
    NME_diag (%, Frontal)
    0.9
    best: 2.68 (CFSS)
  • 3D Face ReconstructiononAFLW-19
    NME_diag (%, Full)
    1.02
    best: 3.92 (CFSS)
  • 3D Face Reconstructionon300W
    NME_inter-ocular (%, Challenge)
    5.3
    best: 8.2 (ASMNet)
  • 3D Face Reconstructionon300W
    NME_inter-ocular (%, Common)
    2.97
    best: 5.09 (3DDFA)
  • 3D Face Reconstructionon300W
    NME_inter-ocular (%, Full)
    3.42
    best: 5.63 (3DDFA)
  • 3D Face ReconstructiononWFLW
    NME (inter-ocular)
    4.62
    best: 11.13 (ESR)

Music8 results

  • Facial Recognition and ModellingonAFLW-19
    AUC_box@0.07 (%, Full)
    79.1
    best: 81.8 (FiFA)
  • Facial Recognition and ModellingonAFLW-19
    NME_box (%, Full)
    1.45
    best: 4.04 (SAN)
  • Facial Recognition and ModellingonAFLW-19
    NME_diag (%, Frontal)
    0.9
    best: 2.68 (CFSS)
  • Facial Recognition and ModellingonAFLW-19
    NME_diag (%, Full)
    1.02
    best: 3.92 (CFSS)
  • Facial Recognition and Modellingon300W
    NME_inter-ocular (%, Challenge)
    5.3
    best: 8.2 (ASMNet)
  • Facial Recognition and Modellingon300W
    NME_inter-ocular (%, Common)
    2.97
    best: 5.09 (3DDFA)
  • Facial Recognition and Modellingon300W
    NME_inter-ocular (%, Full)
    3.42
    best: 5.63 (3DDFA)
  • Facial Recognition and ModellingonWFLW
    NME (inter-ocular)
    4.62
    best: 11.13 (ESR)

Methodology8 results

  • 3Don300W
    NME_inter-ocular (%, Challenge)
    5.3
    best: 8.2 (ASMNet)
  • 3Don300W
    NME_inter-ocular (%, Common)
    2.97
    best: 5.09 (3DDFA)
  • 3Don300W
    NME_inter-ocular (%, Full)
    3.42
    best: 5.63 (3DDFA)
  • 3DonAFLW-19
    AUC_box@0.07 (%, Full)
    79.1
    best: 81.8 (FiFA)
  • 3DonAFLW-19
    NME_box (%, Full)
    1.45
    best: 4.04 (SAN)
  • 3DonAFLW-19
    NME_diag (%, Frontal)
    0.9
    best: 2.68 (CFSS)
  • 3DonAFLW-19
    NME_diag (%, Full)
    1.02
    best: 3.92 (CFSS)
  • 3DonWFLW
    NME (inter-ocular)
    4.62
    best: 11.13 (ESR)

Medical8 results

  • 3D Face ModellingonAFLW-19
    AUC_box@0.07 (%, Full)
    79.1
    best: 81.8 (FiFA)
  • 3D Face ModellingonAFLW-19
    NME_box (%, Full)
    1.45
    best: 4.04 (SAN)
  • 3D Face ModellingonAFLW-19
    NME_diag (%, Frontal)
    0.9
    best: 2.68 (CFSS)
  • 3D Face ModellingonAFLW-19
    NME_diag (%, Full)
    1.02
    best: 3.92 (CFSS)
  • 3D Face Modellingon300W
    NME_inter-ocular (%, Challenge)
    5.3
    best: 8.2 (ASMNet)
  • 3D Face Modellingon300W
    NME_inter-ocular (%, Common)
    2.97
    best: 5.09 (3DDFA)
  • 3D Face Modellingon300W
    NME_inter-ocular (%, Full)
    3.42
    best: 5.63 (3DDFA)
  • 3D Face ModellingonWFLW
    NME (inter-ocular)
    4.62
    best: 11.13 (ESR)