Research map / Image generation / Editing and style transfer
Synthesizing Normalized Faces From Facial Identity Features
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Add morph-based data augmentation to the 1K-image training set (ablation) | Face normalization decoder · LFW | visual fidelity | Helped |
| Use a CNN rather than a fully connected texture decoder (ablation) | Face normalization decoder · LFW | texture detail | Helped |
| Decouple landmark and texture prediction, then warp the texture to the predicted landmarks (ablation) | Face normalization decoder · Test data | reconstruction detail | Helped |
| Add the FaceNet embedding loss (ablation) | Face normalization decoder · LFW | identity preservation: all ... pass; without, about 2% ... would be mid-identified | Helped |
| Use identity features to synthesize normalized faces instead of directly warping the input image | FaceNet-based face normalization · LFW | robustness to pose, lighting, expression, and occlusion | Helped |
Closest papers by meaning
- 3D Facial Expressions through Analysis-by-Neural-Synthesis (CVPR 2024 · similarity 0.87)
- Vec2Face: Scaling Face Dataset Generation with Loosely Constrained Vectors (ICLR 2025 · similarity 0.86)
- Learning Person-Specific Animatable Face Models from In-the-Wild Images via a Shared Base Model (CVPR 2025 · similarity 0.86)
- CapHuman: Capture Your Moments in Parallel Universes (CVPR 2024 · similarity 0.85)
- Data Synthesis with Diverse Styles for Face Recognition via 3DMM-Guided Diffusion (CVPR 2025 · similarity 0.85)
- Neural Implicit Morphing of Face Images (CVPR 2024 · similarity 0.85)
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