EyeSee: Enhancing Art Appreciation through Anthropomorphic Interpretations from Multiple Perspectives

要旨

Art appreciation serves as a crucial medium for emotional communication and sociocultural dialogue. In the digital era, fostering deep user engagement on online art appreciation platforms remains a challenge. Leveraging large language models (LLMs), we present EyeSee, a system designed to engage users through anthropomorphic characters. We implemented and evaluated three modes--Narrator, Artist, and In-Situ--acting as a third-person narrator, a first-person creator, and first-person created objects, respectively, across two sessions: Narrative and Recommendation. We conducted a within-subject study with 24 participants. In the Narrative session, we found that the In-Situ and Artist modes had higher aesthetic appeal than the Narrator mode, although the Artist mode showed lower perceived usability. Additionally, from the Narrative to the Recommendation session, we found that the user-perceived relatability and believability were sustained, but the user-perceived consistency and stereotypicality changed. Our findings suggest novel implications for anthropomorphic character design in enhancing user engagement.

著者
Yongming Li
Xi'an Jiaotong University, Xi'an, China
Hangyue Zhang
University of Illinois Urbana-Champaign, Champaign, Illinois, United States
Andrea Yaoyun Cui
University of Illinois Urbana-Champaign, Champaign, Illinois, United States
Zisong Ma
University of Illinois Urbana-Champaign, Urabana-Champaign, Illinois, United States
Yunpeng Song
Xi'an Jiaotong University, Xi'an, China
Zhongmin Cai
Xi’an Jiaotong University , Xi’an , China
Yun Huang
University of Illinois at Urbana-Champaign, Champaign, Illinois, United States
DOI

10.1145/3706598.3714042

論文URL

https://dl.acm.org/doi/10.1145/3706598.3714042

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: Learning, Creating, and Understanding Art

G416+G417
7 件の発表
2025-04-30 20:10:00
2025-04-30 21:40:00
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