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Research Article | Open Access

Autocompletion of repetitive stroking with image guidance

School of Creative Media, City University of Hong Kong,Tat Chee Avenue, Kowloon, Hong Kong SAR, China
University of Konstanz, University Street 10, 78464 Konstanz, Germany
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Abstract

Image-guided drawing can compensate for a lack of skill but often requires a significant number of repetitive strokes to create textures. Existing automatic stroke synthesis methods are usually limited to predefined styles or require indirect manipulation that may break the spontaneous flow of drawing. We present an assisted drawing system to autocomplete repetitive short strokes during a user’s normal drawing process. Users draw over a reference image as usual; at the same time, our system silently analyzes the input strokes and the reference to infer strokes that follow the user’s input style when certain repetition is detected. Users can accept, modify, or ignore the system’s predictions and continue drawing, thus maintaining fluid control over drawing. Our key idea is to jointly analyze image regions and user input history to detect and predict repetition. The proposed system can effectively reduce the user’s workload when drawing repetitive short strokes, helping users to create results with rich patterns.

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Computational Visual Media
Pages 581-596

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Cite this article:
Chen Y, Kwan KC, Fu H. Autocompletion of repetitive stroking with image guidance. Computational Visual Media, 2023, 9(3): 581-596. https://doi.org/10.1007/s41095-022-0288-2

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Received: 04 March 2022
Accepted: 18 April 2022
Published: 08 March 2023
© The Author(s) 2023.

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduc-tion in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made.

The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.

To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

Other papers from this open access journal are available free of charge from http://www.springer.com/journal/41095. To submit a manuscript, please go to https://www.editorialmanager.com/cvmj.