SVFR: A Unified Framework for Generalized Video Face Restoration (2501.01235v2)

Abstract: Face Restoration (FR) is a crucial area within image and video processing, focusing on reconstructing high-quality portraits from degraded inputs. Despite advancements in image FR, video FR remains relatively under-explored, primarily due to challenges related to temporal consistency, motion artifacts, and the limited availability of high-quality video data. Moreover, traditional face restoration typically prioritizes enhancing resolution and may not give as much consideration to related tasks such as facial colorization and inpainting. In this paper, we propose a novel approach for the Generalized Video Face Restoration (GVFR) task, which integrates video BFR, inpainting, and colorization tasks that we empirically show to benefit each other. We present a unified framework, termed as stable video face restoration (SVFR), which leverages the generative and motion priors of Stable Video Diffusion (SVD) and incorporates task-specific information through a unified face restoration framework. A learnable task embedding is introduced to enhance task identification. Meanwhile, a novel Unified Latent Regularization (ULR) is employed to encourage the shared feature representation learning among different subtasks. To further enhance the restoration quality and temporal stability, we introduce the facial prior learning and the self-referred refinement as auxiliary strategies used for both training and inference. The proposed framework effectively combines the complementary strengths of these tasks, enhancing temporal coherence and achieving superior restoration quality. This work advances the state-of-the-art in video FR and establishes a new paradigm for generalized video face restoration. Code and video demo are available at https://github.com/wangzhiyaoo/SVFR.git.

• The paper introduces the SVFR framework that unifies video blind face restoration, inpainting, and colorization to enhance overall restoration performance.
• It leverages a Task Embedding and Unified Latent Regularization mechanism, combined with facial landmark guidance, to maintain both spatial and temporal consistency.
• Experimental results on VFHQ-test demonstrate superior PSNR, SSIM, and FVD metrics, underscoring significant improvements in video facial restoration quality.

Overview of SVFR: A Unified Framework for Video Face Restoration

The paper presents a novel approach to video face restoration with the introduction of a unified framework termed Stable Video Face Restoration (SVFR). This framework addresses the complex Generalized Video Face Restoration (GVFR) task, which integrates video Blind Face Restoration (BFR), inpainting, and colorization into a cohesive model. These tasks, previously explored individually, are unified here with the intent of leveraging shared information among them to improve overall restoration performance.

Key Contributions and Methodology

The authors propose a unified face restoration framework that incorporates task-specific features and enhances them through a novel Task Embedding and Unified Latent Regularization (ULR) mechanism. The framework enables the model to discern among tasks effectively and uses a joint latent space for all tasks to facilitate feature sharing. The SVFR leverages the generative and motion priors from Stable Video Diffusion (SVD) models, ensuring both spatial-level and motion-level consistency.

To enhance the facial reconstruction fidelity, a Facial Prior Learning (FPL) strategy is employed, using facial landmarks as auxiliary information to guide the model during training. Additionally, a self-referred refinement strategy is implemented during inference to refine video results by referencing previously generated frames, thus improving temporal stability significantly—a challenge that prior video face restoration models struggled with.

Experimental Findings

The proposed SVFR framework undergoes thorough evaluation against state-of-the-art methods on the VFHQ-test dataset comprising various subtasks of GVFR. Quantitative results indicate superior performance across metrics such as PSNR, SSIM, LPIPS, IDS, VIDD, and FVD, showcasing the ability of SVFR to deliver high-quality restorations while maintaining temporal coherence.

The authors provide extensive qualitative comparisons, where SVFR demonstrates improved spatial and temporal consistency, particularly in challenging scenarios such as occlusions, varying lighting conditions, and motion artifacts. SVFR's performance in colorization and inpainting is noted for its stability and accuracy, corroborating the effectiveness of task-level feature integration.

Implications and Future Directions

The introduction of a unified framework for GVFR not only enhances the quality and efficiency of video face restoration tasks but signifies a paradigm shift towards integrated multi-task learning within image and video enhancement realms. The implications are broad, with potential applications across video conferencing, film restoration, and surveillance.

Future work may explore extending the SVFR framework by incorporating more sub-tasks, such as facial attribute editing and expression synthesis. Additionally, further improvements may be achieved by integrating more sophisticated temporal consistency mechanisms or leveraging advanced generative models for enhanced predictive capabilities.

In summary, the SVFR framework represents a significant forward step in video face restoration, exhibiting potential for practical applications and setting a strong foundation for future research and development in video restoration technologies.