MASS: Motion-Aligned Selective Scan for Flow-Based Video Frame Interpolation

Video frame interpolation (VFI) remains a challenging task,particularly when dealing with large, non-linear motions and complexocclusions. While flow-based methods are prevalent, they often strugglewith ambiguous correspondences. Recent VFI methods based on selectiveState Space Models (SSMs) are still limited by static grid-based scanningthat misaligns with physical motion. In this paper, we propose Motion-Aligned Selective Scan (MASS), a novel framework that reformulates fea-ture scanning from static spatial grids to dynamic motion trajectories.MASS builds a feature sequence along each pixel’s flow-guided trajectoryand aggregates it with an SSM. Specifically, we introduce a learnable non-linear path integration to approximate complex curved trajectories viaresidual velocity updates, and a velocity-aware SSM that dynamicallyadjusts the sampling budget and step size based on motion magnitude.This adaptive strategy allocates denser sampling to fast-motion regionswhile keeping static regions efficient. Furthermore, the aggregated statesguide a refinement module to rectify intermediate flows and masks in anend-to-end manner. Extensive experiments indicate that MASS achieveshighly competitive overall performance on standard benchmarks, estab-lishing state-of-the-art results particularly in challenging scenarios withlarge displacements and complex dynamics.