Research map / 3D vision / Face and shape
RAFaRe: Learning Robust and Accurate Non-parametric 3D Face Reconstruction from Pseudo 2D&3D Pairs
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Train PRNet on pseudo 2D&3D pairs instead of original fitted data (ablation) | PRNet · FaceScape-wild, 0°–5° | CD: 2.61 -> 1.97 | Helped |
| Train PRNet on pseudo 2D&3D pairs instead of original fitted data (ablation) | PRNet · FaceScape-wild, 60°–90° | CD: 3.88 -> 6.93 | Hurt |
| Replace PIFu with the base SDF branch (ablation) | Face reconstruction model · FaceScape-wild, 0°–5° | CD: 2.14 -> 1.81 | Helped |
| Add the fine SDF branch (ablation) | RAFaRe · FaceScape-wild, 0°–5° | CD: 1.81 -> 1.79 | Helped |
| Add the fine SDF branch (ablation) | RAFaRe · FaceScape-wild, 60°–90° | CD: 5.04 -> 5.16 | Hurt |
| Add normal-map carving for small-scale geometry (ablation) | RAFaRe · FaceScape-wild, 0°–5° | MNE: 0.062 -> 0.058 | Helped |
| Add normal-map carving for small-scale geometry (ablation) | RAFaRe · FaceScape-wild, 60°–90° | CD: 5.16 -> 5.26 | Hurt |
| Use the complete pseudo-pair-trained, hierarchical SDF reconstruction method | FaceScape-wild · FaceScape-wild, 0°–5° | CD: 2.61 -> 1.79 | Helped |
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- ReDA:Reinforced Differentiable Attribute for 3D Face Reconstruction (CVPR 2020 · similarity 0.85)
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