Core Concepts
Personalized face restoration enhances fidelity and realism by tailoring restoration models to individual identities.
Abstract
The article introduces PFStorer, a method for personalized face restoration using diffusion models. It explores the challenges of face restoration and the benefits of personalization. The approach involves fine-tuning a base restoration model with high-quality reference images to retain identity details while generating realistic images. The method showcases robust capabilities in real-world scenarios and outperforms existing techniques in user studies.
- Introduction:
- Face restoration aims to recover HQ face images from degraded observations.
- Humans are sensitive to subtle differences in facial features that affect identity perception.
- Related work:
- Recent approaches use generative priors like GANs, codebooks, or diffusion models for face restoration.
- Method:
- PFStorer uses personalized adaptation blocks to fine-tune a base model with reference images for faithful restoration.
- Experiments:
- Evaluation on synthetic and real-world data shows PFStorer's superiority in retaining identity features and producing high-quality results.
- Conclusions:
- Personalization improves fidelity and realism in face restoration, showcasing the effectiveness of PFStorer.
Stats
この研究は、ユーザースタディで提案手法が他の手法よりも優れていることを示しています。