InkIdeator: Supporting Chinese-Style Visual Design Ideation via AI-Infused Exploration of Chinese Paintings

要旨

Visual designers often seek inspiration from Chinese paintings when tasked with creating Chinese-style illustrations, posters, etc. Our formative study (N=10) reveals that during ideation, designers learn the cultural symbols, emotions, compositions, and styles in Chinese paintings but face challenges in searching, analyzing, and integrating these dimensions. This paper leverages multi-modal large models to annotate the value of each dimension in 16,315 Chinese paintings, built on which we propose InkIdeator, an ideation support system for Chinese-style visual designs. InkIdeator suggests cultural symbols associated with the task theme, provides dimensional keywords to help analyze Chinese paintings, and generates visual examples integrating user-selected keywords. Our within-subjects study (N=12) using a baseline system without extracted dimensional keywords, along with two extended use cases by Chinese painters, indicates InkIdeator’s effectiveness in creative ideation support, helping users efficiently explore cultural dimensions in Chinese paintings and visualize their ideas. We discuss implications for supporting culture-related visual design ideation with generative AI.

著者
Shiwei Wu
Sun Yat-sen University, Zhuhai, China
Ziyao Gao
Hong Kong University of Science and Technology (Guangzhou), Guangzhou, China/Guangdong, China
Zhendong He
SUN YAT-SEN UNIVERSITY, Zhuhai, Guangdong, China
Zongtan He
Sun Yat-Sen University, Zhuhai, China
ZhuPeng Huang
Sun Yat-sen University, Zhuhai, China
Xia Chen
Sun Yat-sen University, Zhuhai, Guangdong, China
Wei Zeng
The Hong Kong University of Science and Technology (Guangzhou), Guangzhou, Guangdong, China
Xiaojuan Ma
Hong Kong University of Science and Technology, Hong Kong, Hong Kong
Zhenhui Peng
Sun Yat-sen University, Zhuhai, Guangdong Province, China
動画

会議: CHI 2026

ACM CHI Conference on Human Factors in Computing Systems

セッション: Creativity and Innovation

P1 - Room 121
7 件の発表
2026-04-16 20:15:00
2026-04-16 21:45:00