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Axial Generation: A Concretism-Inspired Method for Synthesizing Highly Varied Artworks

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Artificial Intelligence in Music, Sound, Art and Design (EvoMUSART 2021)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 12693))

Abstract

Automated computer generation of aesthetically pleasing artwork has been the subject of research for several decades. The unsolved problem of interest is how to automatically please any audience without too much involvement of the said audience in the process of creation. Two-dimensional pictures have received a lot of attention however, 3D artwork has remained relatively unexplored. This paper introduces the Axial Generation Process (AGP), a versatile generation algorithm that can be employed to create both 2D and 3D items within the Concretism art style. A range of items generated through the AGP were evaluated against a set of formal aesthetic measures. This evaluation shows that the process is capable of generating visually varied items which generally exhibit a diverse range of values across the measures used, in both two and three dimensions.

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Authors and Affiliations

  1. Computer Science, Aston University, Birmingham, UK

    Edward Easton, Anikó Ekárt & Ulysses Bernardet

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  1. Edward Easton
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  2. Anikó Ekárt
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  3. Ulysses Bernardet
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Correspondence to Edward Easton .

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Editors and Affiliations

  1. University of A Coruña, A Coruña, Spain

    Juan Romero

  2. University of Coimbra, Coimbra, Portugal

    Tiago Martins

  3. University of A Coruña, A Coruña, Spain

    Nereida Rodríguez-Fernández

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Easton, E., Ekárt, A., Bernardet, U. (2021). Axial Generation: A Concretism-Inspired Method for Synthesizing Highly Varied Artworks. In: Romero, J., Martins, T., Rodríguez-Fernández, N. (eds) Artificial Intelligence in Music, Sound, Art and Design. EvoMUSART 2021. Lecture Notes in Computer Science(), vol 12693. Springer, Cham. https://doi.org/10.1007/978-3-030-72914-1_8

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  • DOI: https://doi.org/10.1007/978-3-030-72914-1_8

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