# Kalman-Inspired Feature Propagation for Video Face Super-Resolution

arXiv: https://arxiv.org/abs/2408.05205 · HTML: https://mlsanity.com/paper/2408.05205/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 17 references that are in the map.
- First posted on arXiv 9 Aug 2024.

## Builds on (most cited first, 1 of 1)

- [Towards Robust Blind Face Restoration with Codebook Lookup Transformer](https://mlsanity.com/paper/2206.11253/) (2022)

## Built on by (most cited first, 10 of 10)

- [Human Motion Video Generation: A Survey](https://mlsanity.com/paper/2509.03883/) (2025)
- [From Zero to Detail: Deconstructing Ultra-High-Definition Image Restoration from Progressive Spectral Perspective](https://mlsanity.com/paper/2503.13165/) (2025)
- [Efficient Video Face Enhancement with Enhanced Spatial-Temporal Consistency](https://mlsanity.com/paper/2411.16468/) (2024)
- [DicFace: Dirichlet-Constrained Variational Codebook Learning for Temporally Coherent Video Face Restoration](https://mlsanity.com/paper/2506.13355/) (2025)
- [From Blurry to Believable: Enhancing Low-quality Talking Heads with 3D Generative Priors](https://mlsanity.com/paper/2602.06122/) (2026)
- [Differentiable Adaptive Kalman Filtering via Optimal Transport](https://mlsanity.com/paper/2508.07037/) (2025)
- [RGFVR: Reference-Guided Face Video Restoration with Flow Matching](https://mlsanity.com/paper/2606.16401/) (2026)
- [TIGER: Taming Identity, Geometry, and Generative Priors for High-Quality Face Video Restoration](https://mlsanity.com/paper/2606.24336/) (2026)
- [DTI: Dynamic Trajectory Initialization for Generative Face Video Super-Resolution](https://mlsanity.com/paper/2606.29198/) (2026)
- [FADRA: Frequency-Aware Diffusion with Residual Adaptation for Video Face Restoration](https://mlsanity.com/paper/2607.06389/) (2026)

## Abstract (arXiv)

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.