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Research map / 3D vision / Face and shape

Learning Neural Parametric Head Models

CVPR 2023 · 3D vision › Face and shape · approach: 3D morphable face model regression · 57 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Use the proposed local neural-field ensemble with symmetryNPHM · Identity reconstructionL1-Chamfer: 0.200e-2 -> 0.182e-2Helped
Use the proposed local neural-field ensemble with symmetryNPHM · Expression reconstructionL1-Chamfer: 0.299e-2 -> 0.272e-2Helped
Reduce the local ensemble to 12 regions (ablation)NPHM · Identity reconstructionL1-Chamfer: 0.206 -> 0.289Hurt
Reduce the local ensemble to 12 regions (ablation)NPHM · Identity reconstructionF-Score@1.5: 0.938 -> 0.876Hurt
Use 26 regions instead of the full 39-region ensemble (ablation)NPHM · Identity reconstructionL1-Chamfer: 0.206 -> 0.237Hurt
Use 26 regions instead of the full 39-region ensemble (ablation)NPHM · Identity reconstructionF-Score@1.5: 0.938 -> 0.913Hurt
Remove symmetry sharing and the symmetry regularizer (ablation)NPHM · Identity reconstructionL1-Chamfer: 0.206 -> 0.230Hurt
Remove symmetry sharing and the symmetry regularizer (ablation)NPHM · Identity reconstructionNormal consistency: 0.976 -> 0.974Hurt
Remove symmetry sharing and the symmetry regularizer (ablation)NPHM · Identity reconstructionF-Score@1.5: 0.938 -> 0.917Hurt
Replace the local ensemble with a single anchor region (ablation)NPHM · Identity reconstructionL1-Chamfer: 0.206 -> 0.254Hurt
Replace the local ensemble with a single anchor region (ablation)NPHM · Identity reconstructionF-Score@1.5: 0.938 -> 0.906Hurt

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