ArtEvoViewer: A System for Visualizing Interpersonal Influence Among Painters | TU Wien

Information

Publication Type: Conference Paper
Workgroup(s)/Project(s): not specified
Date: October 2025
ISBN: 979-8-3315-7741-4
Publisher: IEEE
Location: Darmstadt
Lecturer: Ryoko Oda
Event: 29th International Conference Information Visualisation (IV)
DOI: 10.1109/IV68685.2025.00041
Booktitle: 2025 29th International Conference Information Visualisation (IV)
Pages: 6
Conference date: 5. August 2025 – 8. August 2025
Pages: 171 – 176
Keywords: artist influence estimation, cultural evolution, digital humanities, paintings, system, visualization

Abstract

Large-scale and objective painting analyses have recently gained attention. In particular, analyzing influence between individual painters requires substantial effort and is hard to reproduce due to subjectivity. Despite increasing demand for automatic estimation, this remains unresolved because such influence is complex and often directional, making it difficult to model. In this paper, we develop an interactive system that visualizes, manipulates, and analyses chains of painterly influence as a network. Using 32,401 paintings, the system infers directional links from color and brushstroke features. The resulting network based on color style features captures stylistic lineages such as landscape-focused and portrait-focused streams, while a multifaceted analysis of Picasso shows that Cézanne's impact appears in brushwork rather than color. Our contributions are twofold: (1) the use of an evolutionary model to assign explicit direction to painter influence and support art historical interpretation, and (2) providing a visualization system that allows dynamic comparison of influence networks based on multiple image features.

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BibTeX

@inproceedings{oda-2025-artevoviewer,
  title =      "ArtEvoViewer: A System for Visualizing Interpersonal
               Influence Among Painters",
  author =     "Ryoko Oda and Eita Nakamura and Daniel Pahr and Henry Ehlers
               and Eduard Gr\"{o}ller and Renata Raidou and Takayuki Itoh",
  year =       "2025",
  abstract =   "Large-scale and objective painting analyses have recently
               gained attention. In particular, analyzing influence between
               individual painters requires substantial effort and is hard
               to reproduce due to subjectivity. Despite increasing demand
               for automatic estimation, this remains unresolved because
               such influence is complex and often directional, making it
               difficult to model. In this paper, we develop an interactive
               system that visualizes, manipulates, and analyses chains of
               painterly influence as a network. Using 32,401 paintings,
               the system infers directional links from color and
               brushstroke features. The resulting network based on color
               style features captures stylistic lineages such as
               landscape-focused and portrait-focused streams, while a
               multifaceted analysis of Picasso shows that C\'{e}zanne's
               impact appears in brushwork rather than color. Our
               contributions are twofold: (1) the use of an evolutionary
               model to assign explicit direction to painter influence and
               support art historical interpretation, and (2) providing a
               visualization system that allows dynamic comparison of
               influence networks based on multiple image features.",
  month =      oct,
  isbn =       "979-8-3315-7741-4",
  publisher =  "IEEE",
  location =   "Darmstadt",
  event =      "29th International Conference Information Visualisation (IV)",
  doi =        "10.1109/IV68685.2025.00041",
  booktitle =  "2025 29th International Conference Information Visualisation
               (IV)",
  pages =      "6",
  pages =      "171--176",
  keywords =   "artist influence estimation, cultural evolution, digital
               humanities, paintings, system, visualization",
  URL =        "https://www.cg.tuwien.ac.at/research/publications/2025/oda-2025-artevoviewer/",
}