Autocomplete painting repetitions

Xing, J; Chen, HT; Wei, LY

Autocomplete painting repetitions

Xing, J Chen, HT

Wei, LY

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Publication Type:: Journal Article
Citation:: ACM Transactions on Graphics, 2014, 33 (6)
Issue Date:: 2014-01-01

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Field	Value	Language
dc.contributor.author	Xing, J	en_US
dc.contributor.author	Chen, HT https://orcid.org/0000-0003-0873-2698	en_US
dc.contributor.author	Wei, LY	en_US
dc.date.issued	2014-01-01	en_US
dc.identifier.citation	ACM Transactions on Graphics, 2014, 33 (6)	en_US
dc.identifier.issn	0730-0301	en_US
dc.identifier.uri	http://hdl.handle.net/10453/116086
dc.description.abstract	2014 Copyright held by the Owner/Author. Publication rights licensed to ACM. (Figure Presented) Painting is a major form of content creation, offering unlimited control and freedom of expression. However, it can involve tedious manual repetitions, such as stippling large regions or hatching complex contours. Thus, a central goal in digital painting research is to automate tedious repetitions while allowing user control. Existing methods impose a sequential order, in which a small exemplar is prepared and then cloned through additional gestures. Such sequential mode may break the continuous, spontaneous flow of painting. Moreover, it is more suitable for homogeneous areas than nuanced variations common in real paintings. We present an interactive digital painting system that autocompletes tedious repetitions while preserving nuanced variations and maintaining natural flows. Specifically, users paint as usual, while our system records and analyzes their workflows. When potential repetition is detected, our system predicts what the user might want to draw and offers auto-completes that adjust to the existing shape-color context. Our method eliminates the need for sequential creation-cloning and better adapts to the local painting contexts. Furthermore, users can choose to accept, ignore, or modify those predictions and thus maintain full control. Our method can be considered as the painting analogy of auto-completes in common typing and IDE systems. We demonstrate the quality and usability of our system through painting results and a pilot user study.	en_US
dc.relation.ispartof	ACM Transactions on Graphics	en_US
dc.relation.isbasedon	10.1145/2661229.2661247	en_US
dc.subject.classification	Software Engineering	en_US
dc.title	Autocomplete painting repetitions	en_US
dc.type	Journal Article
utslib.citation.volume	6	en_US
utslib.citation.volume	33	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
utslib.for	0806 Information Systems	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
pubs.organisational-group	/University of Technology Sydney/Strength - CAI - Centre for Artificial Intelligence
utslib.copyright.status	closed_access
pubs.issue	6	en_US
pubs.publication-status	Published	en_US
pubs.volume	33	en_US

Abstract:

2014 Copyright held by the Owner/Author. Publication rights licensed to ACM. (Figure Presented) Painting is a major form of content creation, offering unlimited control and freedom of expression. However, it can involve tedious manual repetitions, such as stippling large regions or hatching complex contours. Thus, a central goal in digital painting research is to automate tedious repetitions while allowing user control. Existing methods impose a sequential order, in which a small exemplar is prepared and then cloned through additional gestures. Such sequential mode may break the continuous, spontaneous flow of painting. Moreover, it is more suitable for homogeneous areas than nuanced variations common in real paintings. We present an interactive digital painting system that autocompletes tedious repetitions while preserving nuanced variations and maintaining natural flows. Specifically, users paint as usual, while our system records and analyzes their workflows. When potential repetition is detected, our system predicts what the user might want to draw and offers auto-completes that adjust to the existing shape-color context. Our method eliminates the need for sequential creation-cloning and better adapts to the local painting contexts. Furthermore, users can choose to accept, ignore, or modify those predictions and thus maintain full control. Our method can be considered as the painting analogy of auto-completes in common typing and IDE systems. We demonstrate the quality and usability of our system through painting results and a pilot user study.

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http://hdl.handle.net/10453/116086