# Event-Based Motion Magnification

arXiv: https://arxiv.org/abs/2402.11957 · HTML: https://mlsanity.com/paper/2402.11957/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 42 references that are in the map.
- First posted on arXiv 19 Feb 2024.

## Builds on (most cited first, 20 of 20)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Squeeze-and-Excitation Networks](https://mlsanity.com/paper/1709.01507/) (2017)
- [BasicVSR++: Improving Video Super-Resolution with Enhanced Propagation and Alignment](https://mlsanity.com/paper/2104.13371/) (2021)
- [Super SloMo: High Quality Estimation of Multiple Intermediate Frames for Video Interpolation](https://mlsanity.com/paper/1712.00080/) (2017)
- [Learning Enriched Features for Real Image Restoration and Enhancement](https://mlsanity.com/paper/2003.06792/) (2020)
- [End-to-End Learning of Representations for Asynchronous Event-Based Data](https://mlsanity.com/paper/1904.08245/) (2019)
- [Real-Time Intermediate Flow Estimation for Video Frame Interpolation](https://mlsanity.com/paper/2011.06294/) (2020)
- [Real Image Denoising with Feature Attention](https://mlsanity.com/paper/1904.07396/) (2019)
- [TimeLens: Event-based Video Frame Interpolation](https://mlsanity.com/paper/2106.07286/) (2021)
- [Event-Based Fusion for Motion Deblurring with Cross-modal Attention](https://mlsanity.com/paper/2112.00167/) (2021)
- [Time Lens++: Event-based Frame Interpolation with Parametric Non-linear Flow and Multi-scale Fusion](https://mlsanity.com/paper/2203.17191/) (2022)
- [Learning Event-Based Motion Deblurring](https://mlsanity.com/paper/2004.05794/) (2020)
- [GET: Group Event Transformer for Event-Based Vision](https://mlsanity.com/paper/2310.02642/) (2023)
- [Learning-based Video Motion Magnification](https://mlsanity.com/paper/1804.02684/) (2018)
- [TimeReplayer: Unlocking the Potential of Event Cameras for Video Interpolation](https://mlsanity.com/paper/2203.13859/) (2022)
- [A Unified Framework for Event-based Frame Interpolation with Ad-hoc Deblurring in the Wild](https://mlsanity.com/paper/2301.05191/) (2023)
- [Shining light on the DVS pixel: A tutorial and discussion about biasing and optimization](https://mlsanity.com/paper/2304.04706/) (2023)
- [Video Acceleration Magnification](https://mlsanity.com/paper/1704.04186/) (2017)
- [Higher Order Recurrent Neural Networks](https://mlsanity.com/paper/1605.00064/) (2016)
- [Within-Camera Multilayer Perceptron DVS Denoising](https://mlsanity.com/paper/2304.07543/) (2023)

## Built on by (most cited first, 7 of 7)

- [EGVD: Event-Guided Video Diffusion Model for Physically Realistic Large-Motion Frame Interpolation](https://mlsanity.com/paper/2503.20268/) (2025)
- [Event2Audio: Event-Based Optical Vibration Sensing](https://mlsanity.com/paper/2507.03273/) (2025)
- [Event-assisted 12-stop HDR Imaging of Dynamic Scene](https://mlsanity.com/paper/2412.14705/) (2024)
- [LAND: A Longitudinal Analysis of Neuromorphic Datasets](https://mlsanity.com/paper/2602.15973/) (2026)
- [EvMic: Event-based Non-contact sound recovery from effective spatial-temporal modeling](https://mlsanity.com/paper/2504.02402/) (2025)
- [Event-based Visual Deformation Measurement](https://mlsanity.com/paper/2602.14376/) (2026)
- [Hearing the Room Through the Shape of the Drum: Modal-Guided Sound Recovery from Multi-Point Surface Vibrations](https://mlsanity.com/paper/2604.26678/) (2026)

## Abstract (arXiv)

Detecting and magnifying imperceptible high-frequency motions in real-world scenarios has substantial implications for industrial and medical applications. These motions are characterized by small amplitudes and high frequencies. Traditional motion magnification methods rely on costly high-speed cameras or active light sources, which limit the scope of their applications. In this work, we propose a dual-camera system consisting of an event camera and a conventional RGB camera for video motion magnification, providing temporally-dense information from the event stream and spatially-dense data from the RGB images. This innovative combination enables a broad and cost-effective amplification of high-frequency motions. By revisiting the physical camera model, we observe that estimating motion direction and magnitude necessitates the integration of event streams with additional image features. On this basis, we propose a novel deep network tailored for event-based motion magnification. Our approach utilizes the Second-order Recurrent Propagation module to proficiently interpolate multiple frames while addressing artifacts and distortions induced by magnified motions. Additionally, we employ a temporal filter to distinguish between noise and useful signals, thus minimizing the impact of noise. We also introduced the first event-based motion magnification dataset, which includes a synthetic subset and a real-captured subset for training and benchmarking. Through extensive experiments in magnifying small-amplitude, high-frequency motions, we demonstrate the effectiveness and accuracy of our dual-camera system and network, offering a cost-effective and flexible solution for motion detection and magnification.