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Viewing progress in non-photorealistic rendering through Heinlein's lens

Authors:

Bruce S. GoochAuthors Info & Claims

NPAR '10: Proceedings of the 8th International Symposium on Non-Photorealistic Animation and Rendering

Pages 165 - 171

https://doi.org/10.1145/1809939.1809959

Published: 07 June 2010 Publication History

Abstract

The field of non-photorealistic rendering is reaching a mature state. In its infancy, researchers explored the mimicry of methods and tools used by traditional artists to generate works of art, through techniques like watercolor or oil painting simulations. As the field has moved past mimicry, ideas from artists and artistic techniques have been adapted and altered for performance in the media of computer graphics, creating algorithmic aesthetics such as generative art or the automatic composition of objects in a scene, as well as abstraction in rendering and geometry. With these two initial stages of non-photorealistic rendering well established, the field must find new territory to cover. In this paper, we provide a high level overview of the past and current state of non-photorealistic rendering and call to arms the community to create the areas of research that make computation of non-photorealistic rendering generate never before realized results.

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Index Terms

Viewing progress in non-photorealistic rendering through Heinlein's lens
1. Computing methodologies
  1. Computer graphics

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NPAR '10: Proceedings of the 8th International Symposium on Non-Photorealistic Animation and Rendering

June 2010

183 pages

ISBN:9781450301251

DOI:10.1145/1809939

General Chairs:
Morgan McGuire
Williams College
,
John Collomosse
University of Surrey

Copyright © 2010 ACM.

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Published: 07 June 2010

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Bagwari SChoudhary KRaikwar SRana PMighlani S(2023)A Review: The Study and Analysis of Neural Style Transfer in ImageComputer Vision and Machine Intelligence10.1007/978-981-19-7867-8_17(205-220)Online publication date: 6-May-2023
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Rosin PLai YMould DYi RBerger IDoyle LLee SLi CLiu YSemmo AShamir ASon MWinnemöller H(2022)NPRportrait 1.0: A three-level benchmark for non-photorealistic rendering of portraitsComputational Visual Media10.1007/s41095-021-0255-38:3(445-465)Online publication date: 6-Apr-2022
https://doi.org/10.1007/s41095-021-0255-3
Lötzsch WReimann MBüssemeyer MSemmo ADöllner JTrapp M(2022)WISE: Whitebox Image Stylization by Example-Based LearningComputer Vision – ECCV 202210.1007/978-3-031-19790-1_9(135-152)Online publication date: 24-Oct-2022
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Jing YYang YFeng ZYe JYu YSong M(2020)Neural Style Transfer: A ReviewIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2019.292133626:11(3365-3385)Online publication date: 1-Nov-2020
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