SVFR: A Unified Framework for Generalized Video Face Restoration

• URL: http://arxiv.org/abs/2501.01235v2
• Date: Fri, 03 Jan 2025 12:26:32 GMT
• Title: SVFR: A Unified Framework for Generalized Video Face Restoration
• Authors: Zhiyao Wang, Xu Chen, Chengming Xu, Junwei Zhu, Xiaobin Hu, Jiangning Zhang, Chengjie Wang, Yuqi Liu, Yiyi Zhou, Rongrong Ji,
• Abstract summary: Face Restoration (FR) is a crucial area within image and video processing, focusing on reconstructing high-quality portraits from degraded inputs. We propose a novel approach for the Generalized Video Face Restoration task, which integrates video BFR, inpainting, and colorization tasks. This work advances the state-of-the-art in video FR and establishes a new paradigm for generalized video face restoration.
• Score: 86.17060212058452
• License:
• 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.

Related papers

• TDM: Temporally-Consistent Diffusion Model for All-in-One Real-World Video Restoration [13.49297560533422]
  Our method can restore various types of video degradation with a single unified model. Our method advances the video restoration task by providing a unified solution that enhances video quality across multiple applications.
  arXiv  Detail & Related papers  (2025-01-04T12:15:37Z)
• Analysis and Benchmarking of Extending Blind Face Image Restoration to Videos [99.42805906884499]
  We first introduce a Real-world Low-Quality Face Video benchmark (RFV-LQ) to evaluate leading image-based face restoration algorithms. We then conduct a thorough systematical analysis of the benefits and challenges associated with extending blind face image restoration algorithms to degraded face videos. Our analysis identifies several key issues, primarily categorized into two aspects: significant jitters in facial components and noise-shape flickering between frames.
  arXiv  Detail & Related papers  (2024-10-15T17:53:25Z)
• Kalman-Inspired Feature Propagation for Video Face Super-Resolution [78.84881180336744]
  We introduce a novel framework to maintain a stable face prior to time. The Kalman filtering principles offer our method a recurrent ability to use the information from previously restored frames to guide and regulate the restoration process of the current frame. Experiments demonstrate the effectiveness of our method in capturing facial details consistently across video frames.
  arXiv  Detail & Related papers  (2024-08-09T17:57:12Z)
• Towards Real-world Video Face Restoration: A New Benchmark [33.01372704755186]
  We introduce new real-world datasets named FOS with a taxonomy of "Full, Occluded, and Side" faces. FOS datasets cover more diverse degradations and involve face samples from more complex scenarios. We benchmarked both the state-of-the-art BFR methods and the video super resolution (VSR) methods to comprehensively study current approaches.
  arXiv  Detail & Related papers  (2024-04-30T12:37:01Z)
• FLAIR: A Conditional Diffusion Framework with Applications to Face Video Restoration [14.17192434286707]
  We present a new conditional diffusion framework called FLAIR for face video restoration. FLAIR ensures temporal consistency across frames in a computationally efficient fashion. Our experiments show superiority of FLAIR over the current state-of-the-art (SOTA) for video super-resolution, deblurring, JPEG restoration, and space-time frame on two high-quality face video datasets.
  arXiv  Detail & Related papers  (2023-11-26T22:09:18Z)
• Survey on Deep Face Restoration: From Non-blind to Blind and Beyond [79.1398990834247]
  Face restoration (FR) is a specialized field within image restoration that aims to recover low-quality (LQ) face images into high-quality (HQ) face images. Recent advances in deep learning technology have led to significant progress in FR methods.
  arXiv  Detail & Related papers  (2023-09-27T08:39:03Z)
• UniFaceGAN: A Unified Framework for Temporally Consistent Facial Video Editing [78.26925404508994]
  We propose a unified temporally consistent facial video editing framework termed UniFaceGAN. Our framework is designed to handle face swapping and face reenactment simultaneously. Compared with the state-of-the-art facial image editing methods, our framework generates video portraits that are more photo-realistic and temporally smooth.
  arXiv  Detail & Related papers  (2021-08-12T10:35:22Z)
• An Efficient Recurrent Adversarial Framework for Unsupervised Real-Time Video Enhancement [132.60976158877608]
  We propose an efficient adversarial video enhancement framework that learns directly from unpaired video examples. In particular, our framework introduces new recurrent cells that consist of interleaved local and global modules for implicit integration of spatial and temporal information. The proposed design allows our recurrent cells to efficiently propagate-temporal-information across frames and reduces the need for high complexity networks.
  arXiv  Detail & Related papers  (2020-12-24T00:03:29Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.