Cited by 190Digital Media and Visual ArtGenerative Adversarial Networks and Image SynthesisArt, Technology, and Culture

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Large-scale Text-to-Image Generation Models for Visual Artists’ Creative Works

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Ko, Hyung-Kwon, Park, Gwanmo, Jeon, Hyeon, Jo, Jaemin, Kim, Juho, Seo, Jinwook·2023·Proceedings of the 28th International Conference on Intelligent User Interfaces

DOI 10.1145/3581641.3584078

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Abstract

Large-scale Text-to-image Generation Models (LTGMs) (e.g., DALL-E), self-supervised deep learning models trained on a huge dataset, have demonstrated the capacity for generating high-quality open-domain images from multi-modal input. Although they can even produce anthropomorphized versions of objects and animals, combine irrelevant concepts in reasonable ways, and give variation to any user-provided images, we witnessed such rapid technological advancement left many visual artists disoriented in leveraging LTGMs more actively in their creative works. Our goal in this work is to understand how visual artists would adopt LTGMs to support their creative works. To this end, we conducted an interview study as well as a systematic literature review of 72 system/application papers for a thorough examination. A total of 28 visual artists covering 35 distinct visual art domains acknowledged LTGMs’ versatile roles with high usability to support creative works in automating the creation process (i.e., automation), expanding their ideas (i.e., exploration), and facilitating or arbitrating in communication (i.e., mediation). We conclude by providing four design guidelines that future researchers can refer to in making intelligent user interfaces using LTGMs.

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Authors: Ko, H.-K., Park, G., Jeon, H., Jo, J., Kim, J., & Seo, J.
Affiliations: Korea Advanced Institute of Science and Technology (KR)
Seoul National University (KR)
Sungkyunkwan University (KR)
ORCID: Ko, Hyung-Kwon
Park, Gwanmo
Jeon, Hyeon
Jo, Jaemin
Kim, Juho
Seo, Jinwook
Venue: Proceedings of the 28th International Conference on Intelligent User Interfaces
Publisher: ACM
Published: 2023-03-27
Pages: 919-933
DOI: 10.1145/3581641.3584078
OpenAlex: W4321275178
Cited by: 189
References: 71
Citations by year: 2026: 43 · 2025: 70 · 2024: 61 · 2023: 15
FWCI: 55.32
Type: conference paper
Language: en
Open access: bronze
doi.org
Licence: www.acm.org/publications/policies/copyright_policy#Background (vor)
Locations: ACM International Conference on Intelligent User Interfaces (IUI) (publishedVersion)
arXiv (Cornell University) (submittedVersion)
arXiv (Cornell University) (cc-by)
Funders: National Research Foundation of Korea (NRF-2019R1A2C2089062,NRF-2019R1A2C1088900)
Information & communications Technology Planning & Evaluation(IITP) (2019-0-00075,2019-0-00421)
Topics: Physical Sciences › Computer Science › Computer Graphics and Computer-Aided Design › Digital Media and Visual Art
Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Generative Adversarial Networks and Image Synthesis
Social Sciences › Arts and Humanities › Visual Arts and Performing Arts › Art, Technology, and Culture
Keywords: text-to-image generation, visual artists, intelligent user interfaces, design guidelines, DALL-E

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Large-scale Text-to-Image Generation Models for Visual Artists’ Creative Works

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