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

2026-05-28 · Source: Computer Vision and Pattern Recognition · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Emerging Technologies & Innovation · Depth: Expert, quick

Summary

GeoMag is a novel geometric-aware Video Motion Magnification (VMM) framework designed to overcome structural inconsistencies often seen under complex geometric transformations. Traditional learning-based VMM methods, including CNNs and Transformers, struggle with either limited global context or high computational costs. Furthermore, existing training datasets primarily feature simple linear motion, failing to represent real-world geometric and imaging complexities. GeoMag leverages State Space Models to achieve globally consistent motion amplification with linear complexity. To enhance training diversity and realism, the framework introduces Geo-200K, a large-scale synthetic dataset incorporating rich geometric transformations and sensor-realistic degradations. Extensive experiments on both synthetic and real-world benchmarks demonstrate that GeoMag consistently surpasses previous methods in visual fidelity and computational efficiency, while also reducing artifacts and improving structural consistency.

Key takeaway

For Computer Vision Engineers developing robust Video Motion Magnification (VMM) systems, GeoMag presents a significant advancement. You should consider integrating State Space Models to achieve globally consistent motion amplification with linear complexity, especially when dealing with complex geometric transformations. Utilizing the Geo-200K dataset for training can further enhance your models' realism and reduce artifacts, leading to superior visual fidelity and computational efficiency in real-world applications.

Key insights

GeoMag uses State Space Models and a new dataset to improve video motion magnification's geometric consistency and efficiency.

Principles

• VMM needs global context for geometric consistency.
• Training data must reflect real-world geometric complexity.
• State Space Models offer linear complexity for VMM.

Method

GeoMag builds a VMM framework using State Space Models. It constructs Geo-200K, a synthetic dataset with geometric transformations and sensor degradations, to train the model for improved realism and consistency.

In practice

• Use GeoMag for VMM requiring structural consistency.
• Employ Geo-200K for training robust VMM models.
• Consider State Space Models for VMM efficiency.

Topics

• Video Motion Magnification
• State Space Models
• Geometric Transformations
• Computer Vision
• Synthetic Datasets
• Computational Efficiency

Best for: Research Scientist, AI Scientist, Machine Learning Engineer, Computer Vision Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Computer Vision and Pattern Recognition.