Kalman-Inspired Feature Propagation for Video Face Super-Resolution
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.
66b981b88ba60a7a9158a74f
Ruicheng Feng
66b981b88ba60a7a9158a750
Chongyi Li
66b981b88ba60a7a9158a751
Chen Change Loy
Kalman-Inspired Feature Propagation for Video Face Super-Resolution
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.
A Kalman-inspired framework named KEEP enhances video face super-resolution by maintaining temporal consistency and capturing facial details across frames.
Kalman-inspired Feature Propagation KEEP Kalman filtering video face super-resolution temporal consistency facial details face prior recurrent ability