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Gaze-Driven Video Re-Editing

Published: 02 March 2015 Publication History

Abstract

Given the current profusion of devices for viewing media, video content created at one aspect ratio is often viewed on displays with different aspect ratios. Many previous solutions address this problem by retargeting or resizing the video, but a more general solution would re-edit the video for the new display. Our method employs the three primary editing operations: pan, cut, and zoom. We let viewers implicitly reveal what is important in a video by tracking their gaze as they watch the video. We present an algorithm that optimizes the path of a cropping window based on the collected eyetracking data, finds places to cut, and computes the size of the cropping window. We present results on a variety of video clips, including close-up and distant shots, and stationary and moving cameras. We conduct two experiments to evaluate our results. First, we eyetrack viewers on the result videos generated by our algorithm, and second, we perform a subjective assessment of viewer preference. These experiments show that viewer gaze patterns are similar on our result videos and on the original video clips, and that viewers prefer our results to an optimized crop-and-warp algorithm.

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Supplemental movie, appendix, image and software files for, Gaze-Driven Video Re-Editing
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Published In

cover image ACM Transactions on Graphics
ACM Transactions on Graphics  Volume 34, Issue 2
February 2015
136 pages
ISSN:0730-0301
EISSN:1557-7368
DOI:10.1145/2742222
Issue’s Table of Contents
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Association for Computing Machinery

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Publication History

Published: 02 March 2015
Accepted: 01 September 2014
Revised: 01 September 2014
Received: 01 December 2012
Published in TOG Volume 34, Issue 2

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Author Tags

  1. Perceptually-based algorithms
  2. curve fitting
  3. eyetracking
  4. video editing
  5. video retargeting

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  • (2024)AI-Based Cropping of Sport Videos Using SmartCropInternational Journal of Semantic Computing10.1142/S1793351X2445002818:04(637-662)Online publication date: 27-Aug-2024
  • (2024)Real Time GAZED: Online Shot Selection and Editing of Virtual Cameras from Wide-Angle Monocular Video Recordings2024 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)10.1109/WACV57701.2024.00406(4096-4104)Online publication date: 3-Jan-2024
  • (2024)AI-Based Cropping of Ice Hockey Videos for Different Social Media RepresentationsIEEE Access10.1109/ACCESS.2024.344915212(118227-118249)Online publication date: 2024
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