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

• URL: http://arxiv.org/abs/2606.27718v1
• Date: Fri, 26 Jun 2026 04:56:31 GMT
• Title: MASS: Motion-Aligned Selective Scan for Refinement in Flow-Based Video Frame Interpolation
• Abstract summary: Motion-Aligned Selective Scan (MASS) is a novel framework that reformulates feature scanning from static spatial grids to dynamic motion trajectories.<n>MASS builds a feature sequence along each pixel's flow-guided trajectory and aggregates it with an SSM.
• Score: 18.165045289069703
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Video frame interpolation (VFI) remains a challenging task, particularly when dealing with large, non-linear motions and complex occlusions. While flow-based methods are prevalent, they often struggle with ambiguous correspondences. Recent VFI methods based on selective State Space Models (SSMs) are still limited by static grid-based scanning that misaligns with physical motion. In this paper, we propose Motion-Aligned Selective Scan (MASS), a novel framework that reformulates feature scanning from static spatial grids to dynamic motion trajectories. MASS builds a feature sequence along each pixel's flow-guided trajectory and aggregates it with an SSM. Specifically, we introduce a learnable non-linear path integration to approximate complex curved trajectories via residual velocity updates, and a velocity-aware SSM that dynamically adjusts the sampling budget and step size based on motion magnitude. This adaptive strategy allocates denser sampling to fast-motion regions while keeping static regions efficient. Furthermore, the aggregated states guide a refinement module to rectify intermediate flows and masks in an end-to-end manner. Extensive experiments indicate that MASS achieves highly competitive overall performance on standard benchmarks, establishing state-of-the-art results particularly in challenging scenarios with large displacements and complex dynamics.

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