SOTAVerified

Motion Magnification

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.

Papers

Showing 110 of 38 papers

TitleStatusHype
Frequency Decoupling for Motion Magnification via Multi-Level Isomorphic ArchitectureCode2
Event-Based Motion MagnificationCode2
Three-Stream Temporal-Shift Attention Network Based on Self-Knowledge Distillation for Micro-Expression RecognitionCode1
EulerMormer: Robust Eulerian Motion Magnification via Dynamic Filtering within TransformerCode1
STB-VMM: Swin Transformer Based Video Motion MagnificationCode1
FLAVR: Flow-Agnostic Video Representations for Fast Frame InterpolationCode1
Learning-based Video Motion MagnificationCode1
Through a Steerable Lens: Magnifying Neural Network Interpretability via Phase-Based Extrapolation0
AMMSM: Adaptive Motion Magnification and Sparse Mamba for Micro-Expression Recognition0
Image Processing for Motion Magnification0
Show:102550
← PrevPage 1 of 4Next →

No leaderboard results yet.