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

• URL: http://arxiv.org/abs/2605.29762v1
• Date: Thu, 28 May 2026 11:10:58 GMT
• Title: GeoMag: Geometric-Aware Video Motion Magnification via State Space Model
• Abstract summary: Video Motion Magnification (VMM) reveals imperceptible dynamics but often suffers from structural inconsistencies under complex geometric transformations.<n>Existing learning-based methods face a trade-off between the limited global context of CNNs and the high computational cost of Transformers.<n>We propose GeoMag, a geometric-aware VMM framework built upon State Space Models to achieve globally consistent motion amplification with linear complexity.
• Score: 4.244933621272842
• License: http://creativecommons.org/licenses/by/4.0/
• 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 limited global context of CNNs and the high computational cost of Transformers. In addition, current training protocols, largely dominated by simple linear motion, fail to capture the geometric and imaging complexities encountered in real-world videos. To address these issues, we propose GeoMag, a geometric-aware VMM framework built upon State Space Models to achieve globally consistent motion amplification with linear complexity. We further construct Geo-200K, a large-scale synthetic dataset that introduces rich geometric transformations together with sensor-realistic degradations, improving the diversity and realism of training signals. Extensive experiments on synthetic and real-world benchmarks show that GeoMag consistently outperforms prior methods in visual fidelity and computational efficiency, while producing fewer artifacts and better structural consistency.

Related papers

• Learning Geometric Representations from Videos for Spatial Intelligent Multimodal Large Language Models [32.477423778941386]
  We present GeoVR, a framework that learns geometric representations using purely 2D video sequences.<n>Rather than employing superficial feature mixing, GeoVR reshapes the internal representations of the MLLM by distilling geometry knowledge.<n>Experiments on spatial reasoning benchmarks demonstrate that GeoVR achieves state-of-the-art performance.
  arXiv  Detail & Related papers  (2026-06-04T08:11:12Z)
• MotionScale: Reconstructing Appearance, Geometry, and Motion of Dynamic Scenes with Scalable 4D Gaussian Splatting [54.859943475818234]
  MotionScale is a 4D Gaussian Splatting framework that scales efficiently to large scenes and extended sequences.<n>Experiments on challenging real-world benchmarks demonstrate that MotionScale significantly outperforms state-of-the-art methods in both reconstruction quality and temporal stability.
  arXiv  Detail & Related papers  (2026-03-31T06:03:59Z)
• MeshMimic: Geometry-Aware Humanoid Motion Learning through 3D Scene Reconstruction [54.36564144414704]
  MeshMimic is an innovative framework that bridges 3D scene reconstruction and embodied intelligence to enable humanoid robots to learn coupled "motion-terrain" interactions directly from video.<n>By leveraging state-of-the-art 3D vision models, our framework precisely segments and reconstructs both human trajectories and the underlying 3D geometry of terrains and objects.
  arXiv  Detail & Related papers  (2026-02-17T17:09:45Z)
• ContactGaussian-WM: Learning Physics-Grounded World Model from Videos [25.368710400385392]
  We propose ContactGaussian-WM, a differentiable physics-grounded rigid-body world model capable of learning intricate physical laws directly from sparse and contact-rich video sequences.<n>Extensive simulations and real-world evaluations demonstrate that ContactGaussian-WM outperforms state-of-the-art methods in learning complex scenarios.
  arXiv  Detail & Related papers  (2026-02-11T16:48:13Z)
• Scalable Adaptation of 3D Geometric Foundation Models via Weak Supervision from Internet Video [76.32954467706581]
  We propose SAGE, a framework for Scalable Adaptation of GEometric foundation models from raw video streams.<n>We use a hierarchical mining pipeline to transform videos into training trajectories and hybrid supervision.<n>Experiments show that SAGE significantly enhances zero-shot generalization, reducing Chamfer Distance by 20-42% on unseen benchmarks.
  arXiv  Detail & Related papers  (2026-02-08T09:53:21Z)
• GeoVideo: Introducing Geometric Regularization into Video Generation Model [46.38507581500745]
  We introduce geometric regularization losses into video generation by augmenting latent diffusion models with per-frame depth prediction.<n>Our method bridges the gap between appearance generation and 3D structure modeling, leading to improved structural coherence-temporal shape, consistency, and physical plausibility.
  arXiv  Detail & Related papers  (2025-12-03T05:11:57Z)
• Epipolar Geometry Improves Video Generation Models [73.44978239787501]
  3D-consistent video generation could significantly impact numerous downstream applications in generation and reconstruction tasks.<n>We explore how epipolar geometry constraints improve modern video diffusion models.<n>By bridging data-driven deep learning with classical geometric computer vision, we present a practical method for generating spatially consistent videos.
  arXiv  Detail & Related papers  (2025-10-24T16:21:37Z)
• Visual Odometry with Transformers [68.453547770334]
  We introduce Visual odometry Transformer (VoT), which processes sequences of monocular frames by extracting features.<n>Unlike prior methods, VoT directly predicts camera motion without estimating dense geometry and relies solely on camera poses for supervision.<n>VoT scales effectively with larger datasets, benefits substantially from stronger pre-trained backbones, generalizes across diverse camera motions and calibration settings, and outperforms traditional methods while running more than 3 times faster.
  arXiv  Detail & Related papers  (2025-10-02T17:00:14Z)
• DGS-LRM: Real-Time Deformable 3D Gaussian Reconstruction From Monocular Videos [52.46386528202226]
  We introduce the Deformable Gaussian Splats Large Reconstruction Model (DGS-LRM)<n>It is the first feed-forward method predicting deformable 3D Gaussian splats from a monocular posed video of any dynamic scene.<n>It achieves performance on par with state-of-the-art monocular video 3D tracking methods.
  arXiv  Detail & Related papers  (2025-06-11T17:59:58Z)
• Graph Transformers for inverse physics: reconstructing flows around arbitrary 2D airfoils [0.0]
  We introduce a Graph Transformer framework that serves as a general inverse physics engine on meshes.<n>We evaluate this framework on a dataset of steady-state RANS simulations around diverse airfoil geometries.<n>We conduct experiments and provide insights into the relative importance of local geometric processing and global attention mechanisms in mesh-based inverse problems.
  arXiv  Detail & Related papers  (2025-01-28T17:06:09Z)
• GeoDeformer: Geometric Deformable Transformer for Action Recognition [22.536307401874105]
  Vision transformers have recently emerged as an effective alternative to convolutional networks for action recognition. This paper proposes a novel approach, GeoDeformer, designed to capture the variations inherent in action video by integrating geometric comprehension directly into the ViT architecture.
  arXiv  Detail & Related papers  (2023-11-29T16:55:55Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.