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New Mamba-based model enhances video frame interpolation with motion guidance

By PulseAugur Editorial · [1 sources] ·

Researchers have developed Motion-Guided Mamba for Video Frame Interpolation (MGMVFI), a novel adaptation of the selective state space model (SSM) designed to improve video frame interpolation. MGMVFI utilizes Motion-Guided Serialization (MGS) to create a motion-adaptive input order for the SSM, aligning state updates with semantically related tokens for more consistent feature propagation, especially with large or dynamic motions. The model also incorporates contextual synthesis to enhance inter-frame feature synthesis using surrounding spatial context and a lightweight refinement block for detailed reconstruction with reduced computational cost. Experiments show MGMVFI achieves state-of-the-art performance on standard video frame interpolation benchmarks, particularly for complex and dynamic motion sequences. AI

IMPACT Introduces a novel approach to video frame interpolation using state space models, potentially improving performance on dynamic motion sequences.

RANK_REASON Academic paper introducing a new model architecture and method for a specific computer vision task. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CV →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New Mamba-based model enhances video frame interpolation with motion guidance

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Academic paper introducing a new model architecture and method for a specific computer vision task. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CV TIER_1 English(EN) · Jaehyun Park, Nam Ik Cho ·

    Following Motion for Sequential Modeling in Video Frame Interpolation

    arXiv:2608.22861v1 Announce Type: new Abstract: State Space Models (SSMs) have surfaced as a promising architecture in Video Frame Interpolation (VFI), as they can capture long-range dependencies with linear computational complexity. However, their predefined scanning order limit…

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