Data-driven Art
Data-Driven Art is a form of computational art in which the generative process is fundamentally informed and structured by empirical or real-world data sets, distinguishing it from other generative approaches through its reliance on data as both aesthetic and procedural foundation.
69
Publications
2.7K
Citations
254
Authors
112
Institutions
Publications per year
2017–2026
54
69
Large-scale Text-to-Image Generation Models for Visual Artists’ Creative Works
Hyung-Kwon Ko, Gwanmo Park, Hyeon Jeon et al. · 2023 · 165 citations
Creativity in the age of generative AI
Janet Rafner, Roger E. Beaty, James C. Kaufman et al. · Nature Human Behaviour · 2023 · 131 citations
Leah Umansky · Women's studies quarterly · 2012 · 81 citations
Jingwan Lu, Connelly Barnes, Stephen DiVerdi et al. · ACM Transactions on Graphics · 2013 · 79 citations
Arts, Visualization, Digital Art +16
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112
Adobe Systems (United States)
San Jose, United States
4 h-index
8 publications
361 citations
Microsoft Research (United Kingdom)
Cambridge, United Kingdom
3 h-index
3 publications
166 citations

Shanghai Jiao Tong University
Shanghai, China
2 h-index
6 publications
639 citations

Zhejiang University
Hangzhou, China
2 h-index
16 publications
764 citations
Microsoft Research Asia (China)
Beijing, China
2 h-index
4 publications
302 citations
University of Virginia
Charlottesville, United States
2 h-index
2 publications
156 citations

The University of Sydney
Sydney, Australia
2 h-index
4 publications
426 citations
Nanjing University
Nanjing, China
2 h-index
6 publications
153 citations
1–8 of 112
Repository · 9 publications · 287 citations
9
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TOG · 5 publications · 317 citations
5
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2 publications · 66 citations
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2 publications · 170 citations
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2 publications · 44 citations
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