Research map / 3D vision / Face and shape
A 3D Morphable Model Learnt From 10,000 Faces
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use a larger and more demographically varied training set to construct the 3DMM | LSFM-global · MeIn3D test set, face fitting | mean per-vertex reconstruction error | Helped |
| Increase training-set size beyond a few thousand samples | LSFM-global · MeIn3D test set, face fitting | mean per-vertex reconstruction error | No clear effect |
| Use the demographic-specific LSFM-bespoke models instead of LSFM-global | LSFM models · MeIn3D test set | generalisation error | Mixed |
| Use demographic-specific models instead of LSFM-global | LSFM models · MeIn3D test set | generalisation error | Helped |
| Represent faces with LSFM-global rather than BFM | Age classification from 3D shape · MeIn3D test set | F-score: 0.78 -> 0.80 | Helped |
| Represent faces with LSFM-global rather than Brunton et al. model | Age classification from 3D shape · MeIn3D test set | F-score: 0.74 -> 0.80 | Helped |
Closest papers by meaning
- Combining 3D Morphable Models: A Large Scale Face-And-Head Model (CVPR 2019 · similarity 0.91)
- Nonlinear 3D Face Morphable Model (CVPR 2018 · similarity 0.89)
- 3D Face Morphable Models "In-The-Wild" (CVPR 2017 · similarity 0.89)
- i3DMM: Deep Implicit 3D Morphable Model of Human Heads (CVPR 2021 · similarity 0.88)
- FaceScape: A Large-Scale High Quality 3D Face Dataset and Detailed Riggable 3D Face Prediction (CVPR 2020 · similarity 0.87)
- Data Synthesis with Diverse Styles for Face Recognition via 3DMM-Guided Diffusion (CVPR 2025 · similarity 0.85)
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