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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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Graphical Abstract

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.

Keywords

interactionautocompletiondigital drawingpredictiontexture synthesis

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Computational Visual Media
Volume 9 Issue 3,
September 2023
Pages 581-596
DOI: 10.1007/s41095-022-0288-2
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.

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