Research map / 3D vision / Face and shape
FML: Face Model Learning From Videos
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Train with multiple frames instead of monocular frames (ablation) | FML · BU-3DFE | mean geometric error (mm): 1.92\,mm -> 1.76\,mm | Helped |
| Increase training frames from two to four, using one test frame (ablation) | FML · BU-3DFE | mean geometric error (mm): 1.82\,mm -> 1.76\,mm | Helped |
| Use two test frames instead of one after training with two frames (ablation) | FML · BU-3DFE | mean geometric error (mm): 1.82\,mm -> 1.80\,mm | Helped |
| Use four test frames instead of one after training with four frames (ablation) | FML · BU-3DFE | mean geometric error (mm): 1.76\,mm -> 1.74\,mm | Helped |
Closest papers by meaning
- Self-Supervised Multi-Level Face Model Learning for Monocular Reconstruction at Over 250 Hz (CVPR 2018 · similarity 0.91)
- Learning Complete 3D Morphable Face Models From Images and Videos (CVPR 2021 · similarity 0.89)
- MVF-Net: Multi-View 3D Face Morphable Model Regression (CVPR 2019 · similarity 0.89)
- Enhancing Face Recognition With Self-Supervised 3D Reconstruction (CVPR 2022 · similarity 0.89)
- Deep Facial Non-Rigid Multi-View Stereo (CVPR 2020 · similarity 0.88)
- MultiPly: Reconstruction of Multiple People from Monocular Video in the Wild (CVPR 2024 · similarity 0.86)
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