# Learning-based Video Motion Magnification

arXiv: https://arxiv.org/abs/1804.02684 · HTML: https://mlsanity.com/paper/1804.02684/

## Key facts

- Cited by 43 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Apr 2018.

## Built on by (most cited first, 30 of 43)

- [Motion Prompting: Controlling Video Generation with Motion Trajectories](https://mlsanity.com/paper/2412.02700/) (2024)
- [Generative Image Dynamics](https://mlsanity.com/paper/2309.07906/) (2023)
- [Camera Measurement of Physiological Vital Signs](https://mlsanity.com/paper/2111.11547/) (2021)
- [Deep Learning for Micro-expression Recognition: A Survey](https://mlsanity.com/paper/2107.02823/) (2021)
- [Micron-BERT: BERT-based Facial Micro-Expression Recognition](https://mlsanity.com/paper/2304.03195/) (2023)
- [Frequency Decoupling for Motion Magnification via Multi-Level Isomorphic Architecture](https://mlsanity.com/paper/2403.07347/) (2024)
- [EulerMormer: Robust Eulerian Motion Magnification via Dynamic Filtering within Transformer](https://mlsanity.com/paper/2312.04152/) (2023)
- [Feature Representation Learning with Adaptive Displacement Generation and Transformer Fusion for Micro-Expression Recognition](https://mlsanity.com/paper/2304.04420/) (2023)
- [SYNAuG: Exploiting Synthetic Data for Data Imbalance Problems](https://mlsanity.com/paper/2308.00994/) (2023)
- [Generalizable Deepfake Detection with Phase-Based Motion Analysis](https://mlsanity.com/paper/2211.09363/) (2022)
- [Self-Supervised Motion Magnification by Backpropagating Through Optical Flow](https://mlsanity.com/paper/2311.17056/) (2023)
- [Event-Free Moving Object Segmentation from Moving Ego Vehicle](https://mlsanity.com/paper/2305.00126/) (2023)
- [3D Motion Magnification: Visualizing Subtle Motions with Time Varying Radiance Fields](https://mlsanity.com/paper/2308.03757/) (2023)
- [Learning to Rank Onset-Occurring-Offset Representations for Micro-Expression Recognition](https://mlsanity.com/paper/2310.04664/) (2023)
- [Event-Based Motion Magnification](https://mlsanity.com/paper/2402.11957/) (2024)
- [Geometric Graph Representation with Learnable Graph Structure and Adaptive AU Constraint for Micro-Expression Recognition](https://mlsanity.com/paper/2205.00380/) (2022)
- [How Do Deepfakes Move? Motion Magnification for Deepfake Source Detection](https://mlsanity.com/paper/2212.14033/) (2022)
- [VibNet: Vibration-Boosted Needle Detection in Ultrasound Images](https://mlsanity.com/paper/2403.14523/) (2024)
- [FakeTransformer: Exposing Face Forgery From Spatial-Temporal Representation Modeled By Facial Pixel Variations](https://mlsanity.com/paper/2111.07601/) (2021)
- [STB-VMM: Swin Transformer Based Video Motion Magnification](https://mlsanity.com/paper/2302.10001/) (2023)
- [Learning-based Axial Video Motion Magnification](https://mlsanity.com/paper/2312.09551/) (2023)
- [Infused Suppression Of Magnification Artefacts For Micro-AU Detection](https://mlsanity.com/paper/2504.09326/) (2025)
- [MPFNet: A Multi-Prior Fusion Network with a Progressive Training Strategy for Micro-Expression Recognition](https://mlsanity.com/paper/2506.09735/) (2025)
- [Deep Insights of Learning based Micro Expression Recognition: A Perspective on Promises, Challenges and Research Needs](https://mlsanity.com/paper/2210.04935/) (2022)
- [Splannequin: Freezing Monocular Mannequin-Challenge Footage with Dual-Detection Splatting](https://mlsanity.com/paper/2512.05113/) (2025)
- [Revisiting Learning-based Video Motion Magnification for Real-time Processing](https://mlsanity.com/paper/2403.01898/) (2024)
- [SpikeMM: Flexi-Magnification of High-Speed Micro-Motions](https://mlsanity.com/paper/2406.00383/) (2024)
- [Micro-Expression Recognition by Motion Feature Extraction based on Pre-training](https://mlsanity.com/paper/2407.07345/) (2024)
- [Teaching Humans Subtle Differences with DIFFusion](https://mlsanity.com/paper/2504.08046/) (2025)
- [Man versus Machine: AutoML and Human Experts' Role in Phishing Detection](https://mlsanity.com/paper/2108.12193/) (2021)

## Abstract (arXiv)

Video motion magnification techniques allow us to see small motions previously invisible to the naked eyes, such as those of vibrating airplane wings, or swaying buildings under the influence of the wind. Because the motion is small, the magnification results are prone to noise or excessive blurring. The state of the art relies on hand-designed filters to extract representations that may not be optimal. In this paper, we seek to learn the filters directly from examples using deep convolutional neural networks. To make training tractable, we carefully design a synthetic dataset that captures small motion well, and use two-frame input for training. We show that the learned filters achieve high-quality results on real videos, with less ringing artifacts and better noise characteristics than previous methods. While our model is not trained with temporal filters, we found that the temporal filters can be used with our extracted representations up to a moderate magnification, enabling a frequency-based motion selection. Finally, we analyze the learned filters and show that they behave similarly to the derivative filters used in previous works. Our code, trained model, and datasets will be available online.