Research map / 3D vision / Face and shape
Learning Detailed Face Reconstruction From a Single Image
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use the proposed coarse-to-fine reconstruction method instead of the strongest listed baseline | Single-image face reconstruction · Face Recognition Grand Challenge dataset V2 | Ave. Depth Err. [mm]: 3.33 -> 3.22 | Helped |
| Use the proposed coarse-to-fine reconstruction method instead of the strongest listed baseline | Single-image face reconstruction · Face Recognition Grand Challenge dataset V2 | 90% Depth Err. [mm]: 7.02 -> 6.69 | Helped |
| Use two or more VGG-Face blocks rather than one in FineNet (ablation) | FineNet · Supplementary qualitative analysis | Reconstruction quality | Helped |
Closest papers by meaning
- End-To-End 3D Face Reconstruction With Deep Neural Networks (CVPR 2017 · similarity 0.90)
- InverseFaceNet: Deep Monocular Inverse Face Rendering (CVPR 2018 · similarity 0.89)
- Deep 3D Portrait From a Single Image (CVPR 2020 · similarity 0.89)
- Regressing Robust and Discriminative 3D Morphable Models With a Very Deep Neural Network (CVPR 2017 · similarity 0.89)
- Towards High-Fidelity 3D Face Reconstruction From In-the-Wild Images Using Graph Convolutional Networks (CVPR 2020 · similarity 0.89)
- Detailed Facial Geometry Recovery from Multi-View Images by Learning an Implicit Function (AAAI 2022 · similarity 0.89)
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