Video fusion aims to integrate complementary information from multiple source videos while preserving temporal consistency. Effective modeling of temporal dynamics is essential to this goal, yet existing methods incur substantial computational overhead from optical flow estimation and feature warping. In this paper, we present MambaVF, an efficient video fusion framework that uses state space model (SSM) to achieve temporal modeling without explicit motion estimation. First, by formulating video fusion as a sequential state update process, MambaVF captures long-range temporal dependencies with linear complexity, significantly reducing computation and memory costs. Second, the lightweight SSM-based fusion module eliminates conventional flow-guided alignment. Instead, it introduces a mutual state fusion module and a spatio-temporal bidirectional scanning mechanism to enable information aggregation across video streams. Experiments on multiple benchmarks confirm that MambaVF reaches state-of-the-art performance in different video fusion applications (multi-exposure, multi-focus, infrared-visible, medical), while reducing parameters by >90% and FLOPs by >80%, resulting in >50% shorter runtime. Project page: https://mambavf.github.io

翻译：暂无翻译