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

Authors

96

  1. CB
    Connelly Barnes

    Adobe Systems (United States), United States

    2 publications · 116 citations

    2

    h-index

  2. AH
    Aaron Hertzmann

    University of Toronto, Canada

    2 publications · 47 citations

    2

    h-index

  3. AF
    Adam Finkelstein

    Princeton University, United States

    2 publications · 116 citations

    2

    h-index

  4. VL
    Vivian Liu

    Columbia University, United States

    2 publications · 584 citations

    2

    h-index

  5. MD
    Michael D. Murray

    Indiana University – Purdue University Indianapolis, United States

    2 publications · 44 citations

    2

    h-index

1–5 of 96

Venues

  1. arXiv (Cornell University)

    Repository · 5 publications · 144 citations

    5

    h-index

  2. Leonardo

    2 publications · 57 citations

    2

    h-index

  3. ACM Transactions on Graphics

    TOG · 3 publications · 129 citations

    2

    h-index

  4. SSRN Electronic Journal

    Repository · 2 publications · 48 citations

    2

    h-index

  5. IEEE Intelligent Systems

    1 publications · 10 citations

    1

    h-index

1–5