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arxiv: 2606.27718 · v1 · pith:2YWGXGDJnew · submitted 2026-06-26 · 💻 cs.CV

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

Jun-Sang Yoo , Seung-Won Jung This is my paper

Pith reviewed 2026-06-29 04:48 UTC · model grok-4.3

classification 💻 cs.CV
keywords video frame interpolationmotion-aligned scanstate space modelsoptical flowtrajectory integrationflow refinementlarge motion handling
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The pith

Scanning features along motion trajectories refines video frame interpolation for large displacements

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper aims to improve flow-based video frame interpolation by aligning feature scanning with actual motion paths instead of fixed spatial grids. It does this by creating sequences along flow-guided trajectories using learnable non-linear integration for curved paths and a velocity-aware SSM that varies sampling density with motion speed. These aggregated states then help refine the intermediate flows and masks in an end-to-end process. A reader would care if this leads to fewer artifacts in interpolated videos with fast or complex movements.

Core claim

MASS reformulates the scanning process in selective state space models for VFI from static grids to dynamic trajectories guided by optical flow. It introduces learnable non-linear path integration to approximate curved trajectories with residual velocity updates and a velocity-aware SSM that dynamically adjusts sampling budget and step size based on motion magnitude. The aggregated states from this process guide a refinement module to correct intermediate flows and masks.

What carries the argument

Motion-Aligned Selective Scan (MASS), which builds feature sequences along each pixel's flow-guided trajectory and aggregates them with a velocity-aware SSM to guide flow and mask refinement.

If this is right

  • Allocates more sampling to fast-moving regions while being efficient for static areas.
  • Improves handling of large displacements and complex dynamics.
  • Produces states that enable end-to-end rectification of flows and masks.
  • Achieves state-of-the-art results particularly in challenging scenarios.
  • Maintains competitive performance on standard VFI benchmarks.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The approach might apply to other video processing tasks involving motion, such as frame rate conversion or motion compensation.
  • If trajectory estimation is robust, it could reduce error propagation in multi-frame interpolation.
  • Testing on videos with varying motion speeds could reveal optimal parameters for the velocity-aware adjustments.

Load-bearing premise

The flow estimates used to guide the trajectories are sufficiently accurate to align features with true pixel paths.

What would settle it

A comparison experiment on videos with ground-truth large curved motions where MASS shows equivalent or worse PSNR and visual quality than static scanning baselines.

Figures

Figures reproduced from arXiv: 2606.27718 by Jun-Sang Yoo, Seung-Won Jung.

Figure 1
Figure 1. Figure 1: Comparison of feature serialization strategies for VFI. (a) Static raster scanning flattens unaligned grids into mixed sequences, causing ghosting artifacts. (b) MASS extracts features along surrogate trajectories forming motion-consistent sequences and reducing misalignment in dynamic regions. intermediate flows toward time t, warp the input frames, and reconstruct It by blending the warped frames with a … view at source ↗
Figure 2
Figure 2. Figure 2: Overall pipeline of our framework. Features extracted from I0 and I1 are pro￾cessed through a cascade of MASS refinement units in a coarse-to-fine manner, pro￾ducing refined motion and context to synthesize the target frame Iˆt. overall pipeline follows a coarse-to-fine design to robustly handle large non-linear motions and complex occlusions. We first extract feature maps C0 and C1 from I0 and I1 using a … view at source ↗
Figure 3
Figure 3. Figure 3: Detailed illustration of the MASS refinement unit. (a) Non-linear trajectories are constructed from coarse flows through motion-adaptive sampling and learnable residual updates (δvk). (b) Trajectory-aligned feature sequences are aggregated with a velocity-aware scan, which adaptively modulates the discretization step according to the trajectory velocity (|F|/K). (c) The resulting forward and backward conte… view at source ↗
Figure 4
Figure 4. Figure 4: Visual comparison on SNU-FILM [3]. The red arrows highlight regions with large motions and fine details. Finally, visual comparisons in [PITH_FULL_IMAGE:figures/full_fig_p011_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Visualization of the learned trajectory correction. 4.5.2 MASS-Guided Refinement [PITH_FULL_IMAGE:figures/full_fig_p014_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: In-depth analysis of MASS-guided refinement. (a) Average warp error reduction at the coarse 1/8 scale and the fine 1/4 scale for all pixels versus the top 20% high￾motion regions on the SNU-FILM [3] Hard and Extreme splits. (b) Visualizations of internal representations at the coarse 1/8 scale: The input overlay is shown for spatial reference. comparison between the two warp error maps, we visualize the er… view at source ↗
read the original 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.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 0 minor

Summary. The paper proposes MASS, a framework for video frame interpolation that replaces static grid-based scanning in selective SSMs with dynamic feature sequences constructed along flow-guided trajectories. It introduces learnable non-linear path integration via residual velocity updates and a velocity-aware SSM that adjusts sampling budget and step size according to motion magnitude; the resulting states are used to guide end-to-end rectification of intermediate flows and masks. The central claim is that this yields highly competitive performance on standard benchmarks and establishes state-of-the-art results especially under large displacements and complex dynamics.

Significance. If the robustness assumption holds, the work would meaningfully advance flow-based VFI by aligning SSM scanning with physical motion rather than fixed grids, offering a principled way to allocate computation to fast-moving regions while addressing ambiguous correspondences. The adaptive, motion-magnitude-dependent sampling is a concrete and potentially reusable idea.

major comments (2)
  1. [Abstract] Abstract (final paragraph): the performance claim that the aggregated SSM states 'guide a refinement module to rectify intermediate flows and masks' and produce SOTA results in large-displacement regimes rests on the unverified assumption that flow-guided trajectories remain sufficiently accurate even when the initial flow estimates contain errors; no derivation, error-propagation analysis, or targeted experiment demonstrates that residual velocity updates or velocity-aware budget adjustment can recover from such initial misalignment.
  2. [Abstract] Abstract: the assertion of 'establishing state-of-the-art results' is presented without any quantitative metrics, benchmark tables, ablation studies, or error analysis in the manuscript, so the central empirical claim cannot be evaluated from the supplied text.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the insightful comments on the abstract. We agree that the claims require stronger support within the provided text and will revise the abstract accordingly. We address each major comment below.

read point-by-point responses

  1. Referee: [Abstract] Abstract (final paragraph): the performance claim that the aggregated SSM states 'guide a refinement module to rectify intermediate flows and masks' and produce SOTA results in large-displacement regimes rests on the unverified assumption that flow-guided trajectories remain sufficiently accurate even when the initial flow estimates contain errors; no derivation, error-propagation analysis, or targeted experiment demonstrates that residual velocity updates or velocity-aware budget adjustment can recover from such initial misalignment.

    Authors: We acknowledge that the manuscript does not include a dedicated derivation or error-propagation analysis of how residual velocity updates recover from initial flow inaccuracies. The end-to-end training of the refinement module is intended to mitigate such issues, but a targeted experiment isolating robustness to noisy initial flows is absent. We will add this analysis and experiment in the revision. revision: yes

  2. Referee: [Abstract] Abstract: the assertion of 'establishing state-of-the-art results' is presented without any quantitative metrics, benchmark tables, ablation studies, or error analysis in the manuscript, so the central empirical claim cannot be evaluated from the supplied text.

    Authors: The full manuscript contains quantitative results, benchmark comparisons, and ablations in the experiments section. However, the abstract itself presents the SOTA claim without metrics or references to specific results. We will revise the abstract to include key quantitative metrics supporting the claim. revision: yes

Circularity Check

0 steps flagged

No significant circularity; method is an independent proposal

full rationale

The paper presents MASS as a novel framework that reformulates feature scanning along flow-guided trajectories with a velocity-aware SSM and refinement module. No equations, derivations, or self-citations are shown that reduce the claimed performance or states to quantities defined by fitted parameters within the paper itself. The central construction and experimental claims on benchmarks are presented as independent design choices without self-definitional loops, fitted-input predictions, or load-bearing self-citation chains. This is the most common honest finding for a method paper whose claims rest on empirical results rather than closed-form reductions.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract-only review supplies insufficient detail to enumerate free parameters or axioms with precision; the method implicitly relies on standard deep-learning training assumptions and the existence of reliable initial flow estimates.

pith-pipeline@v0.9.1-grok · 5735 in / 1028 out tokens · 22906 ms · 2026-06-29T04:48:35.622726+00:00 · methodology

discussion (0)

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Reference graph

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