VisiFit: Structuring Iterative Improvement for Novice Designers
Lydia B. Chilton, Ecenaz Jen Ozmen, Sam H. Ross, Vivian Liu
Abstract
Fig. 1. Two examples of how the VisiFit system can improve a visual blend prototype in under 4 minutes. The left image blends New York City and autumn. The right image blends navel orange and winter.
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.
CCS Concepts: • Human-centered computing → Interactive systems and tools.
Ask about this paper
Your agent reads all of it.
Lune indexed this paper to the last equation, along with the top-tier papers that cite it. Ask a question and the answer quotes them.
Your agent calls
Luneget_paper_fulltext
Free to start. No credit card required.
Terminal
Install the CLIlune papers fulltext 46fc34e7-8851-4e45-a3f1-d39e0929cf5bCited by top-tier papers9
- PromptCharm: Text-to-Image Generation through Multi-modal Prompting and RefinementZhijie Wang, Yuheng Huang, Da Song, Lei Ma et al.CHI 2024 · 111 citations
- Beyond the Artifact: Power as a Lens for Creativity Support ToolsJingyi Li, Eric Rawn, Jacob Ritchie, Jasper Tran O'Leary et al.UIST 2023 · 79 citations
- PopBlends: Strategies for Conceptual Blending with Large Language ModelsSitong Wang, Savvas Petridis, Taeahn Kwon, Xiaojuan Ma et al.CHI 2023 · 57 citations
- ContextCam: Bridging Context Awareness with Creative Human-AI Image Co-CreationXianzhe Fan, Zihan Wu, Chun Yu, Fenggui Rao et al.CHI 2024 · 49 citations
- TypeDance: Creating Semantic Typographic Logos from Image through Personalized GenerationShishi Xiao, Liangwei Wang, Xiaojuan Ma, Wei ZengCHI 2024 · 35 citations
Builds on2
- Novice-AI Music Co-Creation via AI-Steering Tools for Deep Generative ModelsRyan Louie, Andy Coenen, Cheng Zhi Huang, Michael Terry et al.CHI 2020 · 265 citations
- GRIDS: Interactive Layout Design with Integer ProgrammingNiraj Ramesh Dayama, Kashyap Todi, Taru Saarelainen, Antti OulasvirtaCHI 2020 · 65 citations
Related papers
- Creative Blends of Visual ConceptsZhida Sun, Zhenyao Zhang, Yue Zhang, Min Lu et al.CHI 2025 · 11 citations
- Mixplorer: Scaffolding Design Space Exploration through Genetic Recombination of Multiple Peoples' Designs to Support Novices' CreativityKevin Gonyop Kim, Richard Lee Davis, Alessia Eletta Coppi, Alberto A. P. Cattaneo et al.CHI 2022 · 17 citations
- Perceptual Pat: A Virtual Human Visual System for Iterative Visualization DesignSungbok Shin, Sanghyun Hong, Niklas ElmqvistCHI 2023 · 11 citations
- Color Field: Developing Professional Vision by Visualizing the Effects of Color FiltersMatthew T. Beaudouin-Lafon, Jane L. E, Haijun XiaUIST 2023 · 10 citations
- Bridging the gap to real-world language-grounded visual concept learningWhie Jung, Semin Kim, Junee Kim, Seunghoon HongNeurIPS 2025
