标题： SVFR：一种通用的视频人脸修复框架 显示英文标题

标题： SVFR: A Unified Framework for Generalized Video Face Restoration

摘要： 面部修复（FR）是图像和视频处理中的一个重要领域，专注于从退化的输入中重建高质量的肖像。 尽管在图像FR方面取得了进展，但视频FR仍然相对研究不足，主要是由于时间一致性、运动伪影以及高质量视频数据可用性有限等挑战。 此外，传统的面部修复通常优先提高分辨率，而对相关的任务如面部着色和修补考虑较少。 在本文中，我们提出了一种针对广义视频面部修复（GVFR）任务的新方法，该方法整合了视频BFR、修补和着色任务，并通过实证表明这些任务可以相互受益。 我们提出了一种统一的框架，称为稳定视频面部修复（SVFR），该框架利用了Stable Video Diffusion（SVD）的生成和运动先验，并通过统一的面部修复框架引入任务特定信息。 引入了一个可学习的任务嵌入以增强任务识别。 同时，采用了一种新的统一潜在正则化（ULR）来鼓励不同子任务之间的共享特征表示学习。 为了进一步提高修复质量和时间稳定性，我们引入了面部先验学习和自参照细化作为辅助策略，用于训练和推理。 所提出的框架有效结合了这些任务的互补优势，增强了时间一致性并实现了优越的修复质量。 这项工作推动了视频FR的最先进技术，并为广义视频面部修复建立了一个新范式。 代码和视频演示可在 https://github.com/wangzhiyaoo/SVFR.git 获取。 显示英文摘要

摘要： 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.