Visual Style Prompt Learning Using Diffusion Models for Blind Face Restoration (2412.21042v1)

Abstract: Blind face restoration aims to recover high-quality facial images from various unidentified sources of degradation, posing significant challenges due to the minimal information retrievable from the degraded images. Prior knowledge-based methods, leveraging geometric priors and facial features, have led to advancements in face restoration but often fall short of capturing fine details. To address this, we introduce a visual style prompt learning framework that utilizes diffusion probabilistic models to explicitly generate visual prompts within the latent space of pre-trained generative models. These prompts are designed to guide the restoration process. To fully utilize the visual prompts and enhance the extraction of informative and rich patterns, we introduce a style-modulated aggregation transformation layer. Extensive experiments and applications demonstrate the superiority of our method in achieving high-quality blind face restoration. The source code is available at \href{https://github.com/LonglongaaaGo/VSPBFR}{https://github.com/LonglongaaaGo/VSPBFR}.

• The paper introduces a novel framework for blind face restoration using visual style prompt learning guided by diffusion models to reconstruct high-quality images from degraded inputs.
• A key contribution is the Style-Modulated Aggregation Transformation (SMART) layer, which dynamically adjusts convolutional kernels using visual prompts to enhance feature extraction and capture fine details.
• Extensive experiments demonstrate superior performance on public datasets compared to state-of-the-art methods, improving metrics like FID, PSNR, SSIM, and LPIPS, with potential applications beyond restoration in facial analysis tasks.

Visual Style Prompt Learning Using Diffusion Models for Blind Face Restoration

The paper introduces a novel framework for blind face restoration, leveraging visual style prompt learning through diffusion models. The primary focus is to reconstruct high-quality facial images from degraded inputs without prior knowledge of the degradation processes involved. This task is particularly challenging due to the minimalistic information available in compromised images. The proposed methodology seeks to overcome prior limitations, which often neglect fine details by integrating innovative techniques into the restoration framework.

Methodological Advancements

This work proposes the integration of a visual style prompt learning framework that generates visual prompts in the latent space using diffusion probabilistic models. These prompts are designed to guide the restoration process effectively. A central novel contribution is the inclusion of a style-modulated aggregation transformation (SMART) layer, enhancing the extraction of informative features and detailed patterns. This approach aims to address the shortcomings of prior knowledge-based restoration methods, which primarily focus on geometric priors and facial features that fall short in capturing intricate details necessary for high-quality restoration.

1. Diffusion-Based Style Prompt Module: The framework uses a diffusion-based style prompt module to predict high-quality visual prompts aligned with latent representations in pre-trained models. The paper explores how to encode degraded face images into visual prompts matched to ground-truth images. The process involves denoising steps that estimate clean latent spaces from corrupted inputs, harnessing the capabilities of diffusion probabilistic models.
2. Style-Modulated Aggregation Transformation (SMART) Layer: This component dynamically resizes and adjusts convolutional kernels, utilizing visual prompts to enhance feature extraction. By capturing both local and global contextual information, the SMART layer is pivotal in maximizing the use of facial priors and improving restoration performance.

Experimental Validation

The paper includes extensive empirical validations, comparing the proposed method against various state-of-the-art techniques on public datasets. Results demonstrate superior performance in achieving high-quality blind face restoration across multiple benchmarks. The effectiveness is corroborated through both qualitative and quantitative measures, highlighting the framework's capacity to integrate dense latent representations into the restoration process effectively.

1. Performance Metrics: Key metrics such as Fréchet Inception Distance (FID), PSNR, SSIM, and LPIPS are employed to evaluate restoration quality. The proposed method shows robust improvements in these metrics compared to leading approaches.
2. Applications: Beyond restoration, the framework's application in tasks like facial landmark detection and emotion recognition suggests broader utility. Enhanced accuracy and reduced error rates in these applications underscore the significance of the high-fidelity visual restoration achieved by the model.

Implications and Future Work

This research offers substantial improvements in blind face restoration by utilizing diffusion models for generating rich latent representations that guide restoration tasks. The implications for practical applications are extensive, spanning areas such as video restoration in real-world scenarios and advanced facial analytics.

Future directions may include refining the integration of textual information with visual style prompts for enhanced controllability in face restoration tasks. The potential of expanding this framework to address more diverse and complex scenarios, such as those involving dynamic background changes or motion artifacts in videos, presents an exciting avenue for further research and development within the field. This could bridge the gap between visual and textual understanding in generative models, creating more versatile AI systems capable of sophisticated image manipulations and enhancements.