Modelling Individual Aesthetic Preferences of 3D Sculptures

Edward Easton*, Ulysses Bernardet, Anikó Ekárt

*Corresponding author for this work

Research output: Chapter in Book/Published conference outputConference publication


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.

Original languageEnglish
Title of host publicationArtificial Intelligence in Music, Sound, Art and Design - 13th International Conference, EvoMUSART 2024, Held as Part of EvoStar 2024, Proceedings
EditorsColin Johnson, Sérgio M. Rebelo, Iria Santos
Number of pages16
ISBN (Print)9783031569913
Publication statusPublished - 29 Mar 2024
Event13th International Conference on Artificial Intelligence in Music, Sound, Art and Design, EvoMUSART 2024 held as part of EvoStar 2024 - Aberystwyth, United Kingdom
Duration: 3 Apr 20245 Apr 2024

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume14633 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference13th International Conference on Artificial Intelligence in Music, Sound, Art and Design, EvoMUSART 2024 held as part of EvoStar 2024
Country/TerritoryUnited Kingdom

Bibliographical note

Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.


  • 3D Art Generation
  • Aesthetic judgement
  • Aesthetic modelling


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