title: Kalman-Inspired Feature Propagation for Video Face Super-Resolution
summary: Despite the promising progress of face image super-resolution, video face
  super-resolution remains relatively under-explored. Existing approaches either
  adapt general video super-resolution networks to face datasets or apply
  established face image super-resolution models independently on individual
  video frames. These paradigms encounter challenges either in reconstructing
  facial details or maintaining temporal consistency. To address these issues, we
  introduce a novel framework called Kalman-inspired Feature Propagation (KEEP),
  designed to maintain a stable face prior over 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. Extensive experiments demonstrate the effectiveness of our
  method in capturing facial details consistently across video frames. Code and
  video demo are available at https://jnjaby.github.io/projects/KEEP.
paper_authors._id: 66b981b88ba60a7a9158a74f
paper_authors.name: Ruicheng Feng
paper_authors._id: 66b981b88ba60a7a9158a750
paper_authors.name: Chongyi Li
paper_authors._id: 66b981b88ba60a7a9158a751
paper_authors.name: Chen Change Loy
paper_title: Kalman-Inspired Feature Propagation for Video Face Super-Resolution
paper_summary: Despite the promising progress of face image super-resolution, video face
  super-resolution remains relatively under-explored. Existing approaches either
  adapt general video super-resolution networks to face datasets or apply
  established face image super-resolution models independently on individual
  video frames. These paradigms encounter challenges either in reconstructing
  facial details or maintaining temporal consistency. To address these issues, we
  introduce a novel framework called Kalman-inspired Feature Propagation (KEEP),
  designed to maintain a stable face prior over 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. Extensive experiments demonstrate the effectiveness of our
  method in capturing facial details consistently across video frames. Code and
  video demo are available at https://jnjaby.github.io/projects/KEEP.
paper_ai_summary: A Kalman-inspired framework named KEEP enhances video face super-resolution by maintaining temporal consistency and capturing facial details across frames.
paper_ai_keywords: Kalman-inspired Feature Propagation KEEP Kalman filtering video face super-resolution temporal consistency facial details face prior recurrent ability