GeoDiffMM: Geometry-Guided Conditional Diffusion for Motion Magnification

TL;DR

GeoDiffMM introduces a geometry-guided diffusion framework for video motion magnification that effectively amplifies subtle motions while reducing noise, outperforming existing methods through optical flow conditioning and high-fidelity synthesis.

Contribution

It proposes a novel diffusion-based Lagrangian VMM framework conditioned on optical flow, with a noise-free augmentation strategy and a flow-based synthesis process for improved motion magnification.

Findings

Outperforms state-of-the-art motion magnification methods.

Effectively reduces photon noise interference in micro-motion amplification.

Achieves high-fidelity motion mapping back to the image domain.

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.