{"url":"/task/motion-magnification","name":"Motion Magnification","slug":"motion-magnification","description_markdown":"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.\r\n\r\nThere 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.\r\n\r\nMotion 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.","categories":[{"name":"Computer Vision","url":"/area/computer-vision"}],"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","slug_source":"derived"},"counts":{"papers_tagged":38,"papers_with_code":12,"benchmarks":0,"benchmark_tables_in_archive":0,"benchmark_tables_shown":0,"benchmark_tables_withheld_as_spam":0,"benchmark_definition":"a leaderboard table with at least one row; benchmark_tables_shown also counts the zero-row tables; benchmark_tables_in_archive adds the tables withheld as spam","datasets":0,"subtasks":0,"parent_tasks":0},"benchmarks":[],"datasets":[],"subtasks":[],"parent_tasks":[],"papers":{"order":"repositories listed in the archive (desc), then date (desc); the archive holds no stars","population":"papers tagged with this task that list at least one repository in the archive","shown":12,"of":12,"tagged_in_all":38,"items":[{"url":"/paper/frequency-decoupling-for-motion-magnification","title":"Frequency Decoupling for Motion Magnification via Multi-Level Isomorphic Architecture","date":"2024-03-12","arxiv_id":"2403.07347","repositories_listed":2,"syntology":{"n":14,"n_ran":8,"n_unverified":6,"n_pointer_only":0}},{"url":"/paper/eulermormer-robust-eulerian-motion","title":"EulerMormer: Robust Eulerian Motion Magnification via Dynamic Filtering within Transformer","date":"2023-12-07","arxiv_id":"2312.04152","repositories_listed":2,"syntology":null},{"url":"/paper/learning-based-video-motion-magnification","title":"Learning-based Video Motion Magnification","date":"2018-04-08","arxiv_id":"1804.02684","repositories_listed":2,"syntology":null},{"url":"/paper/skd-tstsan-three-stream-temporal-shift","title":"Three-Stream Temporal-Shift Attention Network Based on Self-Knowledge Distillation for Micro-Expression Recognition","date":"2024-06-25","arxiv_id":"2406.17538","repositories_listed":1,"syntology":null},{"url":"/paper/event-based-motion-magnification","title":"Event-Based Motion Magnification","date":"2024-02-19","arxiv_id":"2402.11957","repositories_listed":1,"syntology":null},{"url":"/paper/motion-magnification-in-robotic-sonography","title":"Motion Magnification in Robotic Sonography: Enabling Pulsation-Aware Artery Segmentation","date":"2023-07-07","arxiv_id":"2307.03698","repositories_listed":1,"syntology":null},{"url":"/paper/stb-vmm-swin-transformer-based-video-motion","title":"STB-VMM: Swin Transformer Based Video Motion Magnification","date":"2023-02-20","arxiv_id":"2302.10001","repositories_listed":1,"syntology":null},{"url":"/paper/multi-domain-learning-for-motion","title":"Multi Domain Learning for Motion Magnification","date":"2023-01-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/lagrangian-motion-magnification-with-double","title":"Lagrangian Motion Magnification with Double Sparse Optical Flow Decomposition","date":"2022-04-15","arxiv_id":"2204.07636","repositories_listed":1,"syntology":null},{"url":"/paper/flavr-flow-agnostic-video-representations-for","title":"FLAVR: Flow-Agnostic Video Representations for Fast Frame Interpolation","date":"2020-12-15","arxiv_id":"2012.08512","repositories_listed":1,"syntology":null},{"url":"/paper/using-phase-instead-of-optical-flow-for","title":"Using phase instead of optical flow for action recognition","date":"2018-09-10","arxiv_id":"1809.03258","repositories_listed":1,"syntology":null},{"url":"/paper/video-acceleration-magnification","title":"Video Acceleration Magnification","date":"2017-04-13","arxiv_id":"1704.04186","repositories_listed":1,"syntology":null}],"syntology_records":1,"syntology_note":"a paper without a record is not a recorded non-run: it may lack an arXiv id or simply be absent from the graph layer"},"description_links":{"kept":0,"unwrapped_to_text":0,"bare_urls_linked":0,"relative_images_dropped":0,"rule":"internal links are kept only when the target slug exists in the catalog"},"syntology":{"read_at":"2026-09-24T18:15:14+00:00","claim":"Per-sample execution status on synthesized fixtures ('ran N of M samples'); not a correctness claim and not a ranking signal.","status_vocabulary":{"ran_honours":"ran, honoured the contract we drafted","ran_violates":"ran, violated the contract we drafted","ran_draft_wrong":"ran; our contract draft was wrong, not the code","ran_fixture":"ran; our fixture could not drive it","ran":"ran on a synthesized input","unverified":"unverified (harvested, no recorded run)"}},"not_shown":{"libraries":"the archive has no per-task library table","trend_sparklines":"the Trend column of the benchmarks table was a rendered image; it is not in the archive","social_and_latest_sorts":"stars and social signals are not in the archive"}}