Motion Magnification
11 papers with code • 0 benchmarks • 0 datasets
Motion magnification is a technique that acts like a microscope for visual motion. It can amplify subtle motions in a video sequence, allowing for visualization of deformations that would otherwise be invisible. To achieve motion magnification, we need to accurately measure visual motions, and group the pixels to be modified.
There are different approaches to motion magnification, such as Lagrangian and Eulerian methods. Lagrangian methods track the trajectories of moving objects and exaggerate them, while Eulerian methods manipulate the motions at fixed positions. Eulerian methods can be further divided into linear and phase-based methods. Linear methods apply a temporal bandpass filter to boost the linear term of a Taylor series expansion of the displacement function, while phase-based methods use complex wavelet transforms to manipulate the phase of the signal.
Motion magnification has various applications, such as measuring the human pulse, visualizing the heat plume of candles, revealing the oscillations of a wine glass, and detecting structural defects.
Benchmarks
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Most implemented papers
Learning-based Video Motion Magnification
We show that the learned filters achieve high-quality results on real videos, with less ringing artifacts and better noise characteristics than previous methods.
Frequency Decoupling for Motion Magnification via Multi-Level Isomorphic Architecture
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.
Video Acceleration Magnification
In these contexts there is often large motion present which severely distorts current video amplification methods that magnify change linearly.
Using phase instead of optical flow for action recognition
We design these complex filters to resemble complex Gabor filters, typically employed for phase-information extraction.
FLAVR: Flow-Agnostic Video Representations for Fast Frame Interpolation
A majority of methods for video frame interpolation compute bidirectional optical flow between adjacent frames of a video, followed by a suitable warping algorithm to generate the output frames.
Lagrangian Motion Magnification with Double Sparse Optical Flow Decomposition
In this paper, we propose a novel approach for local Lagrangian motion magnification of facial micro-motions.
Multi Domain Learning for Motion Magnification
But small motions are prone to noise, illumination changes, large motions, etc.
STB-VMM: Swin Transformer Based Video Motion Magnification
The goal of video motion magnification techniques is to magnify small motions in a video to reveal previously invisible or unseen movement.
Motion Magnification in Robotic Sonography: Enabling Pulsation-Aware Artery Segmentation
To validate the proposed robotic US system for imaging arteries, experiments are carried out on volunteers' carotid and radial arteries.
EulerMormer: Robust Eulerian Motion Magnification via Dynamic Filtering within Transformer
Then, we introduce a novel dynamic filter that eliminates noise cues and preserves critical features in the motion magnification and amplification generation phases.