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About this paper
Venkata Bharath Reddy Reddem, Akshay P Sarashetti, Ranjith Merugu, Amit Satish Unde
CodeFormer++ introduces a modular framework for Blind Face Restoration that resolves the trade-off between visual quality and identity preservation by employing deformable registration and deep metric learning to dynamically fuse features. This framework yields improved perceptual quality and identity fidelity, achieving superior FID scores (e.g., 38.13 on CelebA-Test) and competitive identity preservation (e.g., LMD 5.41 on CelebA-Test) compared to prior methods.
Key insights
- Explicitly decomposing BFR into separate tasks for identity preservation and quality enhancement, followed by a synergistic fusion, effectively mitigates the inherent quality-fidelity trade-off.
- Deformable image registration can be effectively adapted from medical imaging to computer vision tasks like face restoration to precisely align facial features between different image sources.
- A carefully designed deep metric learning strategy, using a synthesized anchor-positive sample and an identity-preserving baseline as a hard negative, is crucial for guiding the network to transfer realistic textures while meticulously preserving identity.
Summary by alphaXiv.
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