A Machine Learning Application Based on Giorgio Morandi Still-Life Paintings to Assist Artists in the Choice of 3D Compositions

Bibliographic Data

ID10271591
AuthorsGuido Salimbeni (0000-0001-6666-3518, Guido Salimbeni, Department of Computing, Goldsmiths University of London, London, SE14 6NW, U.K. Email: [email protected]., corresponding author), Frederic Fol Leymarie (0000-0002-3221-8966, Frederic Fol Leymarie, Department of Computing, Goldsmiths University of London, London, SE14 6NW, U.K. Email: [email protected]., corresponding author), William Latham (0000-0002-8430-222X, William Latham, Department of Computing, Goldsmiths University of London, London, SE14 6NW, U.K. Email: [email protected]., corresponding author)
Year2022
Volume55
Issue1
Pages57-61
Publication date2022-02-23
Open AccessNo
TypeARTICLE
VenueLeonardo (JOURNAL)
Journal identifiersISSN: 0024-094X • E-ISSN: 1530-9282
PublisherMIT Press - Journals (PUBLISHER)
DOI10.1162/leon_a_02073
OpenAlexW3154554016
LanguageEN
References cited13

The authors present a system built to generate arrangements of threedimensional models for aesthetic evaluation, with the aim being to support an artist in their creative process. The authors explore how this system can automatically generate aesthetically pleasing content for use in the media and design industry, based on standards originally developed in master artworks. They then demonstrate the effectiveness of their process in the context of paintings using a collection of images inspired by the work of the artist Giorgio Morandi (Bologna, 1890–1964). Finally, they compare the results of their system with the results of a well-known Generative Adversarial Network (GAN)

Art · Context (archaeology) · Generative grammar · Painting · Process (computing) · Programming language · Visual arts · Aesthetic Perception and Analysis · Artificial Intelligence · Computer Science · Generative Adversarial Networks and Image Synthesis · History

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