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.

@article{Chaudhuri:2013:ACC,
   author = "Siddhartha Chaudhuri and Evangelos Kalogerakis and Stephen Giguere and
      Thomas Funkhouser",
   title = "{AttribIt}: Content Creation with Semantic Attributes",
   journal = "ACM Symposium on User Interface Software and Technology (UIST)",
   year = "2013",
   month = oct
}