Revisiting Learning-based Video Motion Magnification for Real-time Processing

TL;DR

This paper develops a real-time deep learning model for video motion magnification at full-HD resolution, achieving significant speed improvements while maintaining quality by optimizing network architecture and reducing computational complexity.

Contribution

It introduces a novel, efficient neural network architecture for motion magnification that is simpler and faster than previous models, enabling real-time processing of high-resolution videos.

Findings

4.2X fewer FLOPs compared to prior models

2.7X faster processing speed

Maintains comparable magnification quality

Abstract

Video motion magnification is a technique to capture and amplify subtle motion in a video that is invisible to the naked eye. The deep learning-based prior work successfully demonstrates the modelling of the motion magnification problem with outstanding quality compared to conventional signal processing-based ones. However, it still lags behind real-time performance, which prevents it from being extended to various online applications. In this paper, we investigate an efficient deep learning-based motion magnification model that runs in real time for full-HD resolution videos. Due to the specified network design of the prior art, i.e. inhomogeneous architecture, the direct application of existing neural architecture search methods is complicated. Instead of automatic search, we carefully investigate the architecture module by module for its role and importance in the motion magnification task. Two key findings are 1) Reducing the spatial resolution of the latent motion representation in the decoder provides a good trade-off between computational efficiency and task quality, and 2) surprisingly, only a single linear layer and a single branch in the encoder are sufficient for the motion magnification task. Based on these findings, we introduce a real-time deep learning-based motion magnification model with4.2X fewer FLOPs and is 2.7X faster than the prior art while maintaining comparable quality.