GeoDiffMM: Geometry-Guided Conditional Diffusion for Motion Magnification

• URL: http://arxiv.org/abs/2512.08325v1
• Date: Tue, 09 Dec 2025 07:40:51 GMT
• Title: GeoDiffMM: Geometry-Guided Conditional Diffusion for Motion Magnification
• Authors: Xuedeng Liu, Jiabao Guo, Zheng Zhang, Fei Wang, Zhi Liu, Dan Guo,
• Abstract summary: Video Motion Magnification (VMM) amplifies subtle macroscopic motions to a perceptible level.<n>We propose GeoDiffMM, a novel framework conditioned on optical flow as a geometric cue.<n>We show that GeoDiffMM outperforms state-of-the-art methods and significantly improves motion magnification.
• Score: 24.70713855957894
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 frequancey schemes, but they still struggle to separate photon noise from 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 optial 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 while suppressing content-irrelevant perturbations. 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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