# EulerMormer: Robust Eulerian Motion Magnification via Dynamic Filtering within Transformer

arXiv: https://arxiv.org/abs/2312.04152 · HTML: https://mlsanity.com/paper/2312.04152/

## Key facts

- Cited by 27 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 48 references that are in the map.
- First posted on arXiv 7 Dec 2023.

## Builds on (most cited first, 21 of 21)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [The Unreasonable Effectiveness of Deep Features as a Perceptual Metric](https://mlsanity.com/paper/1801.03924/) (2018)
- [Restormer: Efficient Transformer for High-Resolution Image Restoration](https://mlsanity.com/paper/2111.09881/) (2021)
- [Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network](https://mlsanity.com/paper/1609.05158/) (2016)
- [Positive Sample Propagation along the Audio-Visual Event Line](https://mlsanity.com/paper/2104.00239/) (2021)
- [DeepRhythm: Exposing DeepFakes with Attentional Visual Heartbeat Rhythms](https://mlsanity.com/paper/2006.07634/) (2020)
- [KVT: k-NN Attention for Boosting Vision Transformers](https://mlsanity.com/paper/2106.00515/) (2021)
- [Explicit Sparse Transformer: Concentrated Attention Through Explicit Selection](https://mlsanity.com/paper/1912.11637/) (2019)
- [Contrastive Positive Sample Propagation along the Audio-Visual Event Line](https://mlsanity.com/paper/2211.09980/) (2022)
- [Image Demoireing with Learnable Bandpass Filters](https://mlsanity.com/paper/2004.00406/) (2020)
- [Learning-based Video Motion Magnification](https://mlsanity.com/paper/1804.02684/) (2018)
- [Revealing the Invisible with Model and Data Shrinking for Composite-database Micro-expression Recognition](https://mlsanity.com/paper/2006.09674/) (2020)
- [Adaptive Dynamic Filtering Network for Image Denoising](https://mlsanity.com/paper/2211.12051/) (2022)
- [EVREAL: Towards a Comprehensive Benchmark and Analysis Suite for Event-based Video Reconstruction](https://mlsanity.com/paper/2305.00434/) (2023)
- [AnimeSR: Learning Real-World Super-Resolution Models for Animation Videos](https://mlsanity.com/paper/2206.07038/) (2022)
- [ViGT: Proposal-free Video Grounding with Learnable Token in Transformer](https://mlsanity.com/paper/2308.06009/) (2023)
- [Mutual Information-driven Triple Interaction Network for Efficient Image Dehazing](https://mlsanity.com/paper/2308.06998/) (2023)
- [Video Acceleration Magnification](https://mlsanity.com/paper/1704.04186/) (2017)
- [Dual-path TokenLearner for Remote Photoplethysmography-based Physiological Measurement with Facial Videos](https://mlsanity.com/paper/2308.07771/) (2023)
- [GCoNet+: A Stronger Group Collaborative Co-Salient Object Detector](https://mlsanity.com/paper/2205.15469/) (2022)
- [Motion Magnification in Robotic Sonography: Enabling Pulsation-Aware Artery Segmentation](https://mlsanity.com/paper/2307.03698/) (2023)

## Built on by (most cited first, 27 of 27)

- [Frequency Decoupling for Motion Magnification via Multi-Level Isomorphic Architecture](https://mlsanity.com/paper/2403.07347/) (2024)
- [A Semi-supervised Nighttime Dehazing Baseline with Spatial-Frequency Aware and Realistic Brightness Constraint](https://mlsanity.com/paper/2403.18548/) (2024)
- [MMAD: Multi-label Micro-Action Detection in Videos](https://mlsanity.com/paper/2407.05311/) (2024)
- [Micro-gesture Online Recognition using Learnable Query Points](https://mlsanity.com/paper/2407.04490/) (2024)
- [Prototype Learning for Micro-gesture Classification](https://mlsanity.com/paper/2408.03097/) (2024)
- [GRPose: Learning Graph Relations for Human Image Generation with Pose Priors](https://mlsanity.com/paper/2408.16540/) (2024)
- [MM-Gesture: Towards Precise Micro-Gesture Recognition through Multimodal Fusion](https://mlsanity.com/paper/2507.08344/) (2025)
- [Online Micro-gesture Recognition Using Data Augmentation and Spatial-Temporal Attention](https://mlsanity.com/paper/2507.09512/) (2025)
- [Motion Matters: Motion-guided Modulation Network for Skeleton-based Micro-Action Recognition](https://mlsanity.com/paper/2507.21977/) (2025)
- [Exploiting Ensemble Learning for Cross-View Isolated Sign Language Recognition](https://mlsanity.com/paper/2502.02196/) (2025)
- [XInsight: Integrative Stage-Consistent Psychological Counseling Support Agents for Digital Well-Being](https://mlsanity.com/paper/2603.06583/) (2026)
- [3D Smoke Scene Reconstruction Guided by Vision Priors from Multimodal Large Language Models](https://mlsanity.com/paper/2604.05687/) (2026)
- [Task-Generalized Adaptive Cross-Domain Learning for Multimodal Image Fusion](https://mlsanity.com/paper/2508.15505/) (2025)
- [Structure-guided Diffusion Transformer for Low-Light Image Enhancement](https://mlsanity.com/paper/2504.15054/) (2025)
- [Self-supervised Learning Matters: A Simple Ensemble Solution for Micro-Gesture Recognition](https://mlsanity.com/paper/2606.09261/) (2026)
- [Towards Efficient General Feature Prediction in Masked Skeleton Modeling](https://mlsanity.com/paper/2509.03609/) (2025)
- [Rethinking the Role of Feature Engineering and Learning Strategies in Few-Shot Hidden Emotion Recognition](https://mlsanity.com/paper/2606.31249/) (2026)
- [Splannequin: Freezing Monocular Mannequin-Challenge Footage with Dual-Detection Splatting](https://mlsanity.com/paper/2512.05113/) (2025)
- [Multimodal Protein Language Models for Enzyme Kinetic Parameters: From Substrate Recognition to Conformational Adaptation](https://mlsanity.com/paper/2603.12845/) (2026)
- [Joint Spatial-Temporal Modeling and Contrastive Learning for Self-supervised Heart Rate Measurement](https://mlsanity.com/paper/2406.04942/) (2024)
- [AMMSM: Adaptive Motion Magnification and Sparse Mamba for Micro-Expression Recognition](https://mlsanity.com/paper/2503.24057/) (2025)
- [EndoControlMag: Robust Endoscopic Vascular Motion Magnification with Periodic Reference Resetting and Hierarchical Tissue-aware Dual-Mask Control](https://mlsanity.com/paper/2507.15292/) (2025)
- [Training-Free Multimodal Deepfake Detection via Graph Reasoning](https://mlsanity.com/paper/2509.21774/) (2025)
- [GeoDiffMM: Geometry-Guided Conditional Diffusion for Motion Magnification](https://mlsanity.com/paper/2512.08325/) (2025)
- [Beyond Shortcuts: Mitigating Visual Illusions in Frozen VLMs via Qualitative Reasoning](https://mlsanity.com/paper/2604.26250/) (2026)
- [GeoMag: Geometric-Aware Video Motion Magnification via State Space Model](https://mlsanity.com/paper/2605.29762/) (2026)
- [TextGaze: Prompting Gaze Target Estimation with Textual Scene Cues](https://mlsanity.com/paper/2607.10130/) (2026)

## Abstract (arXiv)

Video Motion Magnification (VMM) aims to break the resolution limit of human visual perception capability and reveal the imperceptible minor motion that contains valuable information in the macroscopic domain. However, challenges arise in this task due to photon noise inevitably introduced by photographic devices and spatial inconsistency in amplification, leading to flickering artifacts in static fields and motion blur and distortion in dynamic fields in the video. Existing methods focus on explicit motion modeling without emphasizing prioritized denoising during the motion magnification process. This paper proposes a novel dynamic filtering strategy to achieve static-dynamic field adaptive denoising. Specifically, based on Eulerian theory, we separate texture and shape to extract motion representation through inter-frame shape differences, expecting to leverage these subdivided features to solve this task finely. Then, we introduce a novel dynamic filter that eliminates noise cues and preserves critical features in the motion magnification and amplification generation phases. Overall, our unified framework, EulerMormer, is a pioneering effort to first equip with Transformer in learning-based VMM. The core of the dynamic filter lies in a global dynamic sparse cross-covariance attention mechanism that explicitly removes noise while preserving vital information, coupled with a multi-scale dual-path gating mechanism that selectively regulates the dependence on different frequency features to reduce spatial attenuation and complement motion boundaries. We demonstrate extensive experiments that EulerMormer achieves more robust video motion magnification from the Eulerian perspective, significantly outperforming state-of-the-art methods. The source code is available at https://github.com/VUT-HFUT/EulerMormer.