MAVFusion: Efficient Infrared and Visible Video Fusion via Motion-Aware Sparse Interaction

Infrared and visible video fusion combines the object saliencyfrom infrared images with the texture details from visible images toproduce semantically rich fusion results. However, most existing meth-ods are designed for static image fusion and cannot effectively han-dle frame-to-frame motion in videos. Current video fusion methods im-prove temporal consistency by introducing interactions across frames,but they often require high computational cost. To mitigate these chal-lenges, we propose MAVFusion, an end-to-end video fusion frameworkfeaturing a motion-aware sparse interaction mechanism that enhancesefficiency while maintaining superior fusion quality. Specifically, we lever-age optical flow to identify dynamic regions in multi-modal sequences,adaptively allocating computationally intensive cross-modal attentionto these sparse areas to capture salient transitions and facilitate inter-modal information exchange. For static background regions, a lightweightweak interaction module is employed to maintain structural and appear-ance integrity. By decoupling the processing of dynamic and static re-gions, MAVFusion simultaneously preserves temporal consistency andfine-grained details while significantly accelerating inference. Extensiveexperiments demonstrate that MAVFusion achieves state-of-the-art per-formance on multiple infrared and visible video benchmarks, achieving aspeed of 14.16 FPS at 640 × 480 resolution. The source code is availableat https://github.com/ixilai/MAVFusion.