Kalman-Inspired Feature Propagation for Video Face Super-Resolution
This addresses the problem of inconsistent facial details in video super-resolution for applications like surveillance or entertainment, representing an incremental improvement over existing methods.
The paper tackles video face super-resolution by introducing the Kalman-inspired Feature Propagation (KEEP) framework to maintain temporal consistency and facial details, achieving effective results in experiments.
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.