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.
Abstract (translated)
视频运动放大（VMM）技术能够将微小的宏观运动放大到可以被感知的程度。近年来，现有的主流欧拉方法通过分离纹理、形状和频率等表示学习来解决由放大引起的噪声问题，但当运动位移非常小时，它们仍然难以区分光子噪声与真实微观运动。 我们提出了一种新的基于扩散的方法——GeoDiffMM（几何扩散微动放大框架），该框架以光学流作为几何线索进行条件化处理，从而实现结构一致的运动放大。具体而言，我们设计了无光子噪声的光学流增强策略，通过合成各种非刚性运动场来为模型提供监督信息，这些运动场上没有光子噪声，这有助于模型学习更准确的几何感知光学流，并提高其泛化能力。 接下来，我们开发了一个扩散运动放大器，该放大器在去噪过程中基于（i）作为几何先验条件的光学流和（ii）一个可学习的放大因子控制幅度。这个放大部分能够选择性地放大与场景语义和结构一致的运动成分，并抑制无关的内容扰动。 最后，我们通过基于流的视频合成将放大后的运动映射回图像域，以实现高保真度。 在真实数据集和合成数据集上的广泛实验表明，GeoDiffMM优于现有最先进的方法，并显著提高了运动放大的性能。
URL
https://arxiv.org/abs/2512.08325
