Research map / 3D vision / Face and shape
End-To-End 3D Face Reconstruction With Deep Neural Networks
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use end-to-end UH-E2FAR instead of UH-2FCSL | 3D face reconstruction · UHDB31 | RMSE (mm): 3.37±0.76 -> 2.73±0.71 | Helped |
| Use end-to-end UH-E2FAR instead of UH-2FCSL | 3D face reconstruction · FRGC2 | RMSE (mm): 3.81±2.30 -> 3.71±3.05 | Helped |
| Use end-to-end UH-E2FAR instead of RSNIEF | 3D face reconstruction · BU-3DFE | RMSE (mm): 4.00±1.07 -> 4.52±1.11 | Hurt |
| Add fusion-CNN to the network (ablation) | UH-E2FAR · BU-3DFE | RMSE in key facial regions | Helped |
Closest papers by meaning
- Learning Detailed Face Reconstruction From a Single Image (CVPR 2017 · similarity 0.90)
- Disentangling Features in 3D Face Shapes for Joint Face Reconstruction and Recognition (CVPR 2018 · similarity 0.89)
- GANFIT: Generative Adversarial Network Fitting for High Fidelity 3D Face Reconstruction (CVPR 2019 · similarity 0.89)
- MVF-Net: Multi-View 3D Face Morphable Model Regression (CVPR 2019 · similarity 0.89)
- OFER: Occluded Face Expression Reconstruction (CVPR 2025 · similarity 0.87)
- 3D Facial Expressions through Analysis-by-Neural-Synthesis (CVPR 2024 · similarity 0.87)
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