  Video Motion Magnification (VMM) aims to reveal subtle and imperceptible
motion information of objects in the macroscopic world. Prior methods directly
model the motion field from the Eulerian perspective by Representation Learning
that separates shape and texture or Multi-domain Learning from phase
fluctuations. Inspired by the frequency spectrum, we observe that the
low-frequency components with stable energy always possess spatial structure
and less noise, making them suitable for modeling the subtle motion field. To
this end, we present FD4MM, a new paradigm of Frequency Decoupling for Motion
Magnification with a Multi-level Isomorphic Architecture to capture multi-level
high-frequency details and a stable low-frequency structure (motion field) in
video space. Since high-frequency details and subtle motions are susceptible to
information degradation due to their inherent subtlety and unavoidable external
interference from noise, we carefully design Sparse High/Low-pass Filters to
enhance the integrity of details and motion structures, and a Sparse Frequency
Mixer to promote seamless recoupling. Besides, we innovatively design a
contrastive regularization for this task to strengthen the model's ability to
discriminate irrelevant features, reducing undesired motion magnification.
Extensive experiments on both Real-world and Synthetic Datasets show that our
FD4MM outperforms SOTA methods. Meanwhile, FD4MM reduces FLOPs by 1.63$\times$
and boosts inference speed by 1.68$\times$ than the latest method. Our code is
available at https://github.com/Jiafei127/FD4MM.
