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Temporal anti-aliasing in computer generated animation

Authors:

Jonathan Korein,

Norman BadlerAuthors Info & Claims

ACM SIGGRAPH Computer Graphics, Volume 17, Issue 3

Pages 377 - 388

https://doi.org/10.1145/964967.801168

Published: 01 July 1983 Publication History

Abstract

The desirability of incorporating temporal anti-aliasing, or motion blur, into computer generated animation is discussed and two algorithms for achieving this effect are described. The first approximates continuous object movement and determines intervals during which each object covers each pixel. Hidden surface removal is then performed, allowing the calculation of visible object intensity functions and subsequent filtering. The second form of algorithm detailed involves supersampling the moving image and then filtering the resulting intensity function to “multiply-expose” each output picture. The effects of filter types and the relationship of the algorithms to forms of spatial anti-aliasing are discussed.

References

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Cited By

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Clua ETrevisan DPorcino TMarques BOliveira EBarbosa LLisboa TFerrari VPeres V(2023)Challenges for XR in GamesGrand Research Challenges in Games and Entertainment Computing in Brazil - GranDGamesBR 2020–203010.1007/978-3-031-27639-2_8(159-186)Online publication date: 10-Mar-2023
https://doi.org/10.1007/978-3-031-27639-2_8
Xiang XTian YRengarajan VYoung LZhu BRanjan R(2022)Learning Spatio-Temporal Downsampling for Effective Video UpscalingComputer Vision – ECCV 202210.1007/978-3-031-19797-0_10(162-181)Online publication date: 3-Nov-2022
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Index Terms

Temporal anti-aliasing in computer generated animation
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
  2. Computer graphics

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Information & Contributors

Information

Published In

cover image ACM SIGGRAPH Computer Graphics

ACM SIGGRAPH Computer Graphics Volume 17, Issue 3

July 1983

381 pages

ISSN:0097-8930

DOI:10.1145/964967

Issue’s Table of Contents

SIGGRAPH '83: Proceedings of the 10th annual conference on Computer graphics and interactive techniques
July 1983
420 pages
ISBN:0897911091
DOI:10.1145/800059
Editor:
Peter P. Tanner

Copyright © 1983 ACM.

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]

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 01 July 1983

Published in SIGGRAPH Volume 17, Issue 3

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Tasoren ACelikcan U(2024)NOVAction23Computers and Graphics10.1016/j.cag.2023.10.011118:C(1-10)Online publication date: 1-Feb-2024
https://dl.acm.org/doi/10.1016/j.cag.2023.10.011
Clua ETrevisan DPorcino TMarques BOliveira EBarbosa LLisboa TFerrari VPeres V(2023)Challenges for XR in GamesGrand Research Challenges in Games and Entertainment Computing in Brazil - GranDGamesBR 2020–203010.1007/978-3-031-27639-2_8(159-186)Online publication date: 10-Mar-2023
https://doi.org/10.1007/978-3-031-27639-2_8
Xiang XTian YRengarajan VYoung LZhu BRanjan R(2022)Learning Spatio-Temporal Downsampling for Effective Video UpscalingComputer Vision – ECCV 202210.1007/978-3-031-19797-0_10(162-181)Online publication date: 3-Nov-2022
https://doi.org/10.1007/978-3-031-19797-0_10
Chandrasekaran STripathi BEspindola DPinton G(2021)Modeling Ultrasound Propagation in the Moving Brain: Applications to Shear Shock Waves and Traumatic Brain InjuryIEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control10.1109/TUFFC.2020.302256768:1(201-212)Online publication date: Jan-2021
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Zhu JZhang CLu ZLi X(2021)A multi-resolution deep feature framework for dynamic displacement measurement of bridges using vision-based tracking systemMeasurement10.1016/j.measurement.2021.109847183(109847)Online publication date: Oct-2021
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Rønnow MAssarsson UFratarcangeli M(2021)Fast analytical motion blur with transparencyComputers & Graphics10.1016/j.cag.2021.01.00695(36-46)Online publication date: Apr-2021
https://doi.org/10.1016/j.cag.2021.01.006
Podee NMax NIwasaki KDobashi Y(2021)Temporal and spatial anti-aliasing for rendering reflections on water wavesComputational Visual Media10.1007/s41095-021-0204-17:2(201-215)Online publication date: 3-Mar-2021
https://doi.org/10.1007/s41095-021-0204-1
Yang LLiu SSalvi M(2020)A Survey of Temporal Antialiasing TechniquesComputer Graphics Forum10.1111/cgf.1401839:2(607-621)Online publication date: 13-Jul-2020
https://doi.org/10.1111/cgf.14018
Jiang HNing TBastani B(2020)Efficient Peripheral Flicker Reduction for Foveated Rendering in Mobile VR Systems2020 IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops (VRW)10.1109/VRW50115.2020.00252(803-804)Online publication date: Mar-2020
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Haetinger GGastal E(2020)Regularized Kelvinlet Inversion for Real-Time Image Deformation and Video Time Warping2020 33rd SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI)10.1109/SIBGRAPI51738.2020.00025(124-131)Online publication date: Nov-2020
https://doi.org/10.1109/SIBGRAPI51738.2020.00025
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