Towards Real-World Blind Face Restoration with Generative Facial Prior (2101.04061v2)

Abstract: Blind face restoration usually relies on facial priors, such as facial geometry prior or reference prior, to restore realistic and faithful details. However, very low-quality inputs cannot offer accurate geometric prior while high-quality references are inaccessible, limiting the applicability in real-world scenarios. In this work, we propose GFP-GAN that leverages rich and diverse priors encapsulated in a pretrained face GAN for blind face restoration. This Generative Facial Prior (GFP) is incorporated into the face restoration process via novel channel-split spatial feature transform layers, which allow our method to achieve a good balance of realness and fidelity. Thanks to the powerful generative facial prior and delicate designs, our GFP-GAN could jointly restore facial details and enhance colors with just a single forward pass, while GAN inversion methods require expensive image-specific optimization at inference. Extensive experiments show that our method achieves superior performance to prior art on both synthetic and real-world datasets.

Analysis of "Towards Real-World Blind Face Restoration with Generative Facial Prior"

The paper "Towards Real-World Blind Face Restoration with Generative Facial Prior," authored by Xintao Wang, Yu Li, Honglun Zhang, and Ying Shan, addresses the complex task of blind face restoration in real-world scenarios, particularly focusing on overcoming the challenges of low-quality inputs. It introduces a novel framework, GFP-GAN, which leverages a Generative Facial Prior (GFP) encapsulated in pretrained face GANs like StyleGAN.

Methodology and Contributions

The primary innovation of this paper is the integration of Generative Facial Prior within the blind face restoration pipeline. This approach utilizes the generative capabilities of pretrained GAN models to supplement the restoration process with diverse and rich facial details, previously unseen in low-quality inputs. The GFP is effectively integrated using Channel-Split Spatial Feature Transform (CS-SFT) layers, designed to balance realness and fidelity in the output. This design choice allows the system to maintain identity-preserving properties while enhancing facial details and color.

Key contributions of the work include:

• Leverage of Generative Pprior: The paper utilizes generative models not just for synthesizing realistic outputs but also for providing supplementary rich geometric and texture information.
• Architectural Design: The implementation of CS-SFT layers, which modulate part of the GAN features and leave others unchanged, facilitates a balance between incorporating prior information and retaining high fidelity to the input.
• Joint Restoration and Enhancement: The approach allows simultaneous restoration of facial details and enhancement of facial colors, benefiting colorized output even from potentially monochromatic degraded images.
• Robust Performance: The empirical results demonstrate superior performance over state-of-the-art methods across both synthetic and real-world datasets, evidenced by various qualitative and quantitative benchmarks.

Detailed Results

The paper’s experiments, conducted on diverse datasets including CelebA-Test and several real-world data collections (LFW-Test, CelebChild, and WebPhoto-Test), illustrate GFP-GAN's proficiency in producing visually appealing and identity-preserving results. Notably, the model achieves impressive scores on perceptual metrics such as LPIPS and FID, surpassing competing methods like PSFRGAN and DFDNet.

Critically, the findings of the paper indicate that the inclusion of generative priors, supplemented by CS-SFT layers, results in intricate detail restoration, particularly in regions such as eyes and hair, which traditionally pose challenges in restoration tasks. Additionally, comparisons with GAN inversion approaches highlighted GFP-GAN's advantage in producing high-fidelity outputs more efficiently, without iterative optimization.

Theoretical and Practical Implications

From a theoretical perspective, this paper underscores the potential of integrating generative modeling strategies into restoration processes, advocating for a more holistic approach that considers both semantic and texture aspects of images. Practically, the findings suggest new avenues for real-world applications in fields such as historical image restoration, surveillance, and other domains requiring image enhancement from poor quality originals.

Future Directions

While the paper successfully demonstrates the efficacy of generative facial priors, it acknowledges the challenge of further minimizing artifact introduction during restoration. Future work could explore refining degradation models to better approximate real-world conditions, as well as extending the technique to broader applications beyond face restoration.

In conclusion, this paper makes substantive strides in face restoration methodology, pushing the boundaries of GAN applications and proposing a significant step towards practical blind restoration systems.