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Modelling Individual Aesthetic Preferences of 3D Sculptures

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Artificial Intelligence in Music, Sound, Art and Design (EvoMUSART 2024)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14633))

Abstract

Aesthetic preference is a complex puzzle with many subjective aspects. This subjectivity makes it incredibly difficult to computationally model aesthetic preference for an individual. Despite this complexity, individual aesthetic preference is an important part of life, impacting a multitude of aspects, including romantic and platonic relationships, decoration, product choices and artwork. Models of aesthetic preference form the basis of automated and semi-automated Evo-Art systems. These range from looking at individual aspects to more complex models considering multiple, different criteria. Effectively modelling aesthetic preference greatly increases the potential impact of these systems. This paper presents a flexible computational model of aesthetic preference, primarily focusing on generating 3D sculptures. Through demonstrating the model using several examples, it is shown that the model is flexible enough to identify and respond to individual aesthetic preferences, handling the subjectivity at the root of aesthetic preference and providing a good base for further extension to strengthen the ability of the system to model individual aesthetic preference.

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Authors and Affiliations

  1. Aston Centre for AI Research and Application (ACAIRA), Aston University, Birmingham, UK

    Edward Easton, Ulysses Bernardet & Anikó Ekárt

Authors
  1. Edward Easton
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  2. Ulysses Bernardet
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  3. Anikó Ekárt
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Corresponding author

Correspondence to Edward Easton .

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Editors and Affiliations

  1. University of Nottingham, Nottingham, UK

    Colin Johnson

  2. University of Coimbra, Coimbra, Portugal

    Sérgio M. Rebelo

  3. University of Coruña, Coruña, Spain

    Iria Santos

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Easton, E., Bernardet, U., Ekárt, A. (2024). Modelling Individual Aesthetic Preferences of 3D Sculptures. In: Johnson, C., Rebelo, S.M., Santos, I. (eds) Artificial Intelligence in Music, Sound, Art and Design. EvoMUSART 2024. Lecture Notes in Computer Science, vol 14633. Springer, Cham. https://doi.org/10.1007/978-3-031-56992-0_9

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  • DOI: https://doi.org/10.1007/978-3-031-56992-0_9

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-56991-3

  • Online ISBN: 978-3-031-56992-0

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