Deep Learning-based Face Video Restoration Technique: A Survey

With the rapid development of video communication and social media, face video restoration (FVR) has become a prominent research area, with significant breakthroughs driven by deep learning techniques. However, to date, no comprehensive survey has systematically reviewed deep learning-based FVR methods. To bridge this gap and support future research, we present a comprehensive survey of deep learning-based FVR methods. We begin by providing a brief introduction to the FVR task. Next, we review commonly used datasets, evaluation metrics and loss functions in the field. We then categorize FVR tasks based on different types of degradation and systematically review representative methods from three perspectives: network architecture, temporal modeling strategies, and facial detail enhancement. Finally, we discuss current challenges and outline potential directions for future research. This survey aims to serve as a valuable reference for researchers and practitioners who are interested in advancing face video restoration technologies.