Generative Visual Art
Generative Visual Art is a computational method that autonomously produces visual artworks through algorithmic processes guided by predefined rules, stochastic models, or learned data distributions, distinguishing itself from traditional and interactive art forms by emphasizing autonomy, emergence, and procedural generation in the creation of aesthetic outputs.
31
Publications
1.7K
Citations
96
Authors
44
Institutions
Publications per year
2017–2026
18
Parents
31
The artist emerges: Visual art learning alters neural structure and function
Alexander Schlegel, Prescott Alexander, Sergey V. Fogelson et al. · NeuroImage · 2014 · 117 citations
Jingwan Lu, Connelly Barnes, Stephen DiVerdi et al. · ACM Transactions on Graphics · 2013 · 79 citations
Arts, Visualization, Digital Art +16
1–5 of 31
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Indiana University – Purdue University Indianapolis, United States
2 publications · 44 citations
2
h-index
1–5 of 96
44
Adobe Systems (United States)
San Jose, United States
4 h-index
7 publications
316 citations

Zhejiang University
Hangzhou, China
3 h-index
6 publications
442 citations
Princeton University
Princeton, United States
2 h-index
5 publications
269 citations
Hong Kong Baptist University
Hong Kong, Hong Kong
2 h-index
2 publications
141 citations

University of Hong Kong
Pok Fu Lam, Hong Kong
2 h-index
3 publications
151 citations
University of Kentucky
Lexington, United States
2 h-index
2 publications
44 citations
The University of Texas at Dallas
Richardson, United States
1 h-index
2 publications
54 citations

Universidade Estadual de Campinas (UNICAMP)
Campinas, Brazil
1 h-index
1 publications
13 citations
1–8 of 44
Repository · 5 publications · 144 citations
5
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2 publications · 57 citations
2
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TOG · 3 publications · 129 citations
2
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Repository · 2 publications · 48 citations
2
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1 publications · 10 citations
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1–5