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GeoMag uses State Space Models for consistent video motion magnification

Researchers have developed GeoMag, a new framework for video motion magnification that utilizes State Space Models to enhance imperceptible dynamics while maintaining global structural consistency. This approach addresses limitations of existing methods, such as CNNs' limited context and Transformers' high computational cost, by offering linear complexity. To improve training for complex real-world scenarios, a large-scale synthetic dataset called Geo-200K was created, featuring diverse geometric transformations and sensor-realistic degradations. Experiments demonstrate GeoMag's superior performance in visual fidelity, computational efficiency, and artifact reduction compared to previous techniques. AI

RANK_REASON This is a research paper describing a new method for video motion magnification. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CV →

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GeoMag uses State Space Models for consistent video motion magnification

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This is a research paper describing a new method for video motion magnification. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CV TIER_1 Italiano(IT) · Kecheng Han, Yuchen Zhang, Bingqing Liu, Boqiang Guo, Wenbin Zheng, Shiyuan Pei ·

    GeoMag: Geometric-Aware Video Motion Magnification via State Space Model

    arXiv:2605.29762v1 Announce Type: new Abstract: Video Motion Magnification (VMM) reveals imperceptible dynamics but often suffers from structural inconsistencies under complex geometric transformations. Existing learning-based methods generally face a trade-off between the limite…