GeoDiffMM: Geometry-Guided Conditional Diffusion for Motion Magnification

Xuedeng Liu; Jiabao Guo; Zheng Zhang; Fei Wang; Zhi Liu; Dan Guo

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arXiv · 2512.08325

GeoDiffMM: Geometry-Guided Conditional Diffusion for Motion Magnification

Xuedeng Liu·Jiabao Guo·Zheng Zhang·Fei Wang·Zhi Liu·Dan Guo

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Abstract

Video Motion Magnification (VMM) amplifies subtle macroscopic motions to a perceptible level. Recently, existing mainstream Eulerian approaches address amplification-induced noise via decoupling representation learning such as texture, shape and frequency schemes, but they still struggle to mitigate the interference of photon noise on true micro-motion when motion displacements are very small. We propose GeoDiffMM, a novel diffusion-based Lagrangian VMM framework conditioned on optical flow as a geometric cue, enabling structurally consistent motion magnification. Specifically, we design a Noise-Free Optical Flow Augmentation strategy that synthesizes diverse nonrigid motion fields without photon noise as supervision, helping the model learn more accurate geometry-aware optical flow and generalize better. Next, we develop a Diffusion Motion Magnifier that conditions the denoising process on (i) optical flow as a geometry prior and (ii) a learnable magnification factor controlling magnitude, thereby selectively amplifying motion components consistent with scene semantics and structure. Finally, we perform Flow-based Video Synthesis to map the amplified motion back to the image domain with high fidelity. Extensive experiments on real and synthetic datasets show that GeoDiffMM outperforms state-of-the-art methods and significantly improves motion magnification.

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BibTeX RIS

Xuedeng Liu, Jiabao Guo, Zheng Zhang, Fei Wang, Zhi Liu, Dan Guo. 2026-03-24. GeoDiffMM: Geometry-Guided Conditional Diffusion for Motion Magnification. https://arxiv.org/abs/2512.08325

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