VisiFit: Structuring Iterative Improvement for Novice Designers
Video
Team Information
Team Members
Faculty Advisor: Lydia Chilton, Assistant Professor of Computer Science, Columbia University
Ecenaz Ozmen, Undergraduate Student in Computer Science, Columbia College
Sam Ross, Undergraduate Student in Computer Science, Barnard College
Vivian Liu, PhD Student in Computer Science, Columbia University
Abstract
Visual blends are a graphic design challenge to seamlessly integrate two objects into one. Existing tools help novices create prototypes of blends, but it is unclear how they would improve them to be higher fidelity. To help novices, we aim to add structure to the iterative improvement process.
We introduce a technique for improving blends called fundamental dimension decomposition. It is grounded in principles of human visual object recognition.
We present VisiFit - a computational design system that uses this technique to enable novice graphic designers to improve blends by exploring a structured design space with computationally generated options they can select, adjust, and chain together.
Our evaluation shows novices can substantially improve 76% of blends in under 4 minutes.
We discuss how the technique can be generalized to other blending problems, and how computational tools can support novices by enabling them to explore a structured design space quickly and efficiently.Contact this Team
Team Contact: Ecenaz Ozmen (use form to send email)
