  Video motion magnification amplifies invisible small motions to be
perceptible, which provides humans with a spatially dense and holistic
understanding of small motions in the scene of interest. This is based on the
premise that magnifying small motions enhances the legibility of motions. In
the real world, however, vibrating objects often possess convoluted systems
that have complex natural frequencies, modes, and directions. Existing motion
magnification often fails to improve legibility since the intricate motions
still retain complex characteristics even after being magnified, which may
distract us from analyzing them. In this work, we focus on improving 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. To achieve this, we propose a novel Motion Separation Module that
enables to disentangle and magnify the motion representation along axes of
interest. Furthermore, 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 by adding a new feature: user
controllability. Axial motion magnification is a more generalized concept;
thus, our method can be directly adapted to the generic motion magnification
and achieves favorable performance against competing methods.
