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Autocompletion of repetitive stroking with image guidance

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

55 Downloads (CityUHK Scholars)

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.
Original languageEnglish
Pages (from-to)581–596
Number of pages16
JournalComputational Visual Media
Volume9
Issue number3
Online published8 Mar 2023
DOIs
Publication statusPublished - Sept 2023

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 3 - Good Health and Well-being
    SDG 3 Good Health and Well-being

Research Keywords

  • interaction
  • autocompletion
  • digital drawing
  • prediction
  • texture synthesis

Publisher's Copyright Statement

  • This full text is made available under CC-BY 4.0. https://creativecommons.org/licenses/by/4.0/

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