Video motion magnification amplifies invisible small motions to be perceptible, which provides humans with spatially dense and holistic understanding about small motions from the scene of interest. This is based on the premise that magnifying small motions enhances the legibility of the motion. In the real world, however, vibrating objects often possess complex systems, having complex natural frequencies, modes, and directions. Existing motion magnification often fails to improve the legibility since the intricate motions still retain complex characteristics even when magnified, which distracts us from analyzing them. In this work, we focus on improving the legibility by proposing a new concept, axial motion magnification, which magnifies decomposed motions along the user-specified direction. Axial motion magnification can be applied to various applications where motions of specific axes are critical, by providing simplified and easily readable motion information. We propose a novel learning-based axial motion magnification method with the Motion Separation Module that enables to disentangle and magnify the motion representation along axes of interest. Further, we build a new synthetic training dataset for the axial motion magnification task. Our proposed method improves the legibility of resulting motions along certain axes, while adding additional user controllability. Our method can be directly adopted to the generic motion magnification and achieves favorable performance against competing methods. Our project page is available at https://axial-momag.github.io/axial-momag/.

翻译：视频运动放大技术可将不可见的微小运动放大至可感知的程度，从而提供对感兴趣场景中微小运动的空间密集且整体的理解。其前提是放大微小运动能增强运动的可读性。然而，现实世界中振动物体通常具有复杂系统，包含复杂的固有频率、模态和方向。现有运动放大技术常因放大后的复杂运动仍保留原特征而干扰分析，未能有效提升可读性。本研究通过提出“轴向运动放大”这一新概念来改善可读性，该技术可沿用户指定方向放大分解后的运动。轴向运动放大通过提供简化且易于读取的运动信息，可应用于特定轴向运动至关重要的多种场景。我们提出了一种新颖的基于学习的轴向运动放大方法，其运动分离模块能够解耦并放大沿目标轴的运动表征。此外，我们为轴向运动放大任务构建了新的合成训练数据集。所提方法不仅增强了特定轴向运动结果的可读性，还增加了额外的用户可控性。该方法可直接应用于通用运动放大任务，并在与现有方法的对比中展现出优越性能。项目主页：https://axial-momag.github.io/axial-momag/。