Autocompletion of repetitive stroking with image guidance

Computational Visual Media

Research Article

Yilan Chen¹  Hongbo Fu¹  Kin Chung Kwan² 

[1] 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;

关键词: interaction;    autocompletion;    digital drawing;    prediction;    texture synthesis;   

DOI  :  10.1007/s41095-022-0288-2

 received in 2022-03-04, accepted in 2022-04-18,  发布年份 2022

来源: Springer

【 摘 要 】

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.

【 授权许可】

CC BY   
© The Author(s) 2023

【 图 表 】

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