Research map / Image generation / Editing and style transfer
Cross-Modal Deep Face Normals With Deactivable Skip Connections
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use cross-modal learning with image and normal encoders/decoders and deactivable skip connections | Face normal estimation · Photoface | mean angular error (degrees): 24.8$\pm$6.8 -> 22.8$\pm$6.5 | Helped |
| Use cross-modal learning with image and normal encoders/decoders and deactivable skip connections | Face normal estimation · Florence | mean angular error (degrees): 14.1$\pm$2.16 -> 11.3$\pm$1.5 | Helped |
| Fine-tune on Photoface | Ours · Photoface | mean angular error (degrees): 22.8$\pm$6.5 -> 12.0$\pm$5.3 | Helped |
| Remove skip connections (ablation) | Cross-modal face normal model · Photoface and Florence | normal estimation accuracy | Hurt |
| Remove the normal encoder (ablation) | Cross-modal face normal model · Photoface and Florence | normal estimation accuracy | Hurt |
| Remove the normal encoder and image decoder (ablation) | Cross-modal face normal model · In-the-wild face images | normal estimation accuracy | Hurt |
Closest papers by meaning
- Face Normals "In-The-Wild" Using Fully Convolutional Networks (CVPR 2017 · similarity 0.88)
- r-BTN: Cross-Domain Face Composite and Synthesis From Limited Facial Patches (AAAI 2018 · similarity 0.85)
- InverseFaceNet: Deep Monocular Inverse Face Rendering (CVPR 2018 · similarity 0.84)
- Learning Oracle Attention for High-Fidelity Face Completion (CVPR 2020 · similarity 0.84)
- UMFN: Unified Multi-Domain Face Normalization for Joint Cross-domain Prototype Learning and Heterogeneous Face Recognition (CVPR 2025 · similarity 0.83)
- Connect, Collapse, Corrupt: Learning Cross-Modal Tasks with Uni-Modal Data (ICLR 2024 · similarity 0.83)
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