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Attribit: content creation with semantic attributes

Author(s): Chaudhuri, Siddhartha; Kalogerakis, Evangelos; Giguere, Stephen; Funkhouser, Thomas

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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.
Publication Date: Oct-2013
Citation: Chaudhuri, Siddhartha, Evangelos Kalogerakis, Stephen Giguere, and Thomas Funkhouser. "Attribit: content creation with semantic attributes." In Proceedings of the 26th annual ACM symposium on User interface software and technology (2013): pp. 193-202. doi:10.1145/2501988.2502008
DOI: 10.1145/2501988.2502008
Pages: 193 - 202
Type of Material: Conference Article
Journal/Proceeding Title: Proceedings of the 26th annual ACM symposium on User interface software and technology
Version: Author's manuscript

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