Attriblt: Content creation with semantic attributes

Siddhartha Chaudhuri, Evangelos Kalogerakis, Stephen Giguere, Thomas Funkhouser

Research output: Chapter in Book/Report/Conference proceedingConference contribution

77 Scopus citations

Abstract

We present ATTRIBIT, an approach for people to create visual content using relative semantic attributes expressed in linguistic terms. During an off-line processing step, ATTRIBIT learns semantic attributes for design components that reflect the high-level intent people may have for creating content in a domain (e.g. adjectives such as "dangerous", "scary" or "strong") and ranks them according to the strength of each learned attribute. Then, during an interactive design session, a person can explore different combinations of visual components using commands based on relative attributes (e.g. "make this part more dangerous"). Novel designs are assembled in real-time as the strengths of selected attributes are varied, enabling rapid, in-situ exploration of candidate designs. We applied this approach to 3D modeling and web design. Experiments suggest this interface is an effective alternative for novices performing tasks with high-level design goals.

Original languageEnglish (US)
Title of host publicationUIST 2013 - Proceedings of the 26th Annual ACM Symposium on User Interface Software and Technology
Pages193-202
Number of pages10
DOIs
StatePublished - 2013
Event26th Annual ACM Symposium on User Interface Software and Technology, UIST 2013 - St. Andrews, United Kingdom
Duration: Oct 8 2013Oct 11 2013

Publication series

NameUIST 2013 - Proceedings of the 26th Annual ACM Symposium on User Interface Software and Technology

Other

Other26th Annual ACM Symposium on User Interface Software and Technology, UIST 2013
Country/TerritoryUnited Kingdom
CitySt. Andrews
Period10/8/1310/11/13

All Science Journal Classification (ASJC) codes

  • Software

Keywords

  • Assembly-based modeling
  • Content creation
  • Exploratory interfaces
  • High-level attributes
  • Interactive modeling
  • Relative attributes
  • Semantic attributes

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