Creative Blends of Visual Concepts

要旨

Visual blends combine elements from two distinct visual concepts into a single, integrated image, with the goal of conveying ideas through imaginative and often thought-provoking visuals. Communicating abstract concepts through visual blends poses a series of conceptual and technical challenges. To address these challenges, we introduce Creative Blends, an AI-assisted design system that leverages metaphors to visually symbolize abstract concepts by blending disparate objects. Our method harnesses commonsense knowledge bases and large language models to align designers’ conceptual intent with expressive concrete objects. Additionally, we employ generative text-to-image techniques to blend visual elements through their overlapping attributes. A user study (N=24) demonstrated that our approach reduces participants’ cognitive load, fosters creativity, and enhances the metaphorical richness of visual blend ideation. We explore the potential of our method to expand visual blends to include multiple object blending and discuss the insights gained from designing with generative AI.

著者
Zhida Sun
Shenzhen University, Shenzhen, China
Zhenyao Zhang
Shenzhen University, Shenzhen, China
Yue Zhang
Shenzhen University, Shenzhen, China
Min Lu
Shenzhen University, Shenzhen, Guangdong, China
Dani Lischinski
Hebrew University, Jerusalem, Israel
Daniel CohenOr
Shenzhen University, Shenzhen, China
Hui Huang
Shenzhen University, Shenzhen, China
DOI

10.1145/3706598.3713683

論文URL

https://dl.acm.org/doi/10.1145/3706598.3713683

動画

会議: CHI 2025

The ACM CHI Conference on Human Factors in Computing Systems (https://chi2025.acm.org/)

セッション: Image and AI

G303
7 件の発表
2025-04-28 23:10:00
2025-04-29 00:40:00
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