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Practical temporal consistency for image-based graphics applications

Published: 01 July 2012 Publication History

Abstract

We present an efficient and simple method for introducing temporal consistency to a large class of optimization driven image-based computer graphics problems. Our method extends recent work in edge-aware filtering, approximating costly global regularization with a fast iterative joint filtering operation. Using this representation, we can achieve tremendous efficiency gains both in terms of memory requirements and running time. This enables us to process entire shots at once, taking advantage of supporting information that exists across far away frames, something that is difficult with existing approaches due to the computational burden of video data. Our method is able to filter along motion paths using an iterative approach that simultaneously uses and estimates per-pixel optical flow vectors. We demonstrate its utility by creating temporally consistent results for a number of applications including optical flow, disparity estimation, colorization, scribble propagation, sparse data up-sampling, and visual saliency computation.

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Published In

cover image ACM Transactions on Graphics
ACM Transactions on Graphics  Volume 31, Issue 4
July 2012
935 pages
ISSN:0730-0301
EISSN:1557-7368
DOI:10.1145/2185520
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 ACM 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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Publication History

Published: 01 July 2012
Published in TOG Volume 31, Issue 4

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Author Tags

  1. filtering
  2. optical flow
  3. regularization
  4. temporal coherence
  5. video

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  • (2024)SaCo Loss: Sample-Wise Affinity Consistency for Vision-Language Pre-Training2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)10.1109/CVPR52733.2024.02583(27348-27359)Online publication date: 16-Jun-2024
  • (2024)Deep Video Harmonization by Improving Spatial-temporal ConsistencyMachine Intelligence Research10.1007/s11633-023-1447-321:1(46-54)Online publication date: 15-Jan-2024
  • (2023)Interactive Control over Temporal Consistency while Stylizing Video StreamsComputer Graphics Forum10.1111/cgf.1489142:4Online publication date: 26-Jul-2023
  • (2023)Blind Video Deflickering by Neural Filtering with a Flawed Atlas2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)10.1109/CVPR52729.2023.01006(10439-10448)Online publication date: Jun-2023
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