GFPGAN
Paper - Towards Real-World Blind Face Restoration with Generative Facial Prior
Key Notes
- GFP-GAN that leverages rich and diverse priors encapsulated in a pretrained face GAN
- StyleGAN - generating faithful faces with a high degree of variability
- GFP-GAN with CS-SFT layers achieves a good balance of fidelity and texture faithfulness
- Generative facial priors (GFP)
Image Restoration Techniques
- Super-resolution
- Denoising
- Deblurring
- Compression removal
- GFP-GAN is comprised of a degradation removal module (U-Net) and a pretrained face GAN (such as StyleGAN2)
- Reconstruction loss that constraints the outputs yˆ close to the ground-truth
- Adversarial loss for restoring realistic textures
- Identity preserving loss.
State-of-the-art face restoration methods
- HiFaceGAN
- DFDNet
- PSFRGAN
- Super-FAN
- DeblurGANv2
- ESRGAN
- Real-ESRGAN
- BasicSR
- FaceXLib
- HandyView
- GFPGAN Inference Demo (Paper Model)
- GFPGAN Inference Demo
- GFPGAN (CVPR 2021)
- More Projects - Link
Keep Reading!!!
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