Concepedia

Data-driven Generative Art

Data-Driven Generative Art is a form of procedural art in which the generative process is governed by external data sources, distinguishing it through its reliance on empirical or real-world datasets to shape or drive the artistic output.

1

Publications

11

Citations

1

Authors

1

Institutions

Publications per year

2017–2026

1

Parents

Publications

1

1–3 of 1

Authors

1

  1. WS
    Weili Shi

    Parsons School of Design, United States

    1 publications · 11 citations

    1

    h-index

  2. AP
    Antonio Pošćić

    1 publications · 10 citations

    0

    h-index

  3. GK
    Gordan Kreković

    University of Zagreb, Croatia

    1 publications · 10 citations

    0

    h-index

  4. JX
    Jianheng Xiang

    China Academy of Art, China

    1 publications · 10 citations

    0

    h-index

1–4 of 1

Institutions

1

  1. Parsons School of Design

    New York, United States

    1 h-index

    1 publications

    11 citations

  2. China Academy of Art

    Hangzhou, China

    1 publications

    10 citations

1–2 of 1

Venues

  1. IEEE Computer Graphics and Applications

    1 publications · 11 citations

    1

    h-index

  3. Visual Informatics

    1 publications · 10 citations

1–3