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Reimagining Benin Bronzes using generative adversarial networks.

Authors :
Atairu, Minne
Source :
AI & Society; Feb2024, Vol. 39 Issue 1, p91-102, 12p
Publication Year :
2024

Abstract

In this paper, I describe my artistic project, Igùn—a StyleGAN series trained to animate the research question: what bronze objects could have been produced should the 1897 British invasion not have occurred in the Benin Kingdom? In addition to looting over 3000 palace-commissioned artworks, I surmise that the invasion resulted in a 17-year (1897–1914) artistic decline. Although post-invasion colonial reports referred to a thriving art scene and increased colonial art patronage, there is a dearth of visual documentation to identify objects created during this period. Considering this absence, I propose Igùn, a series of StyleGAN models trained on a dataset of looted Benin Bronzes. This project is informed by the Igún Eronmwon's (the royal guild of bronze casters) artistic protocols. Finally, I present three prototypes based on emergent themes—infancy and facial expressions, which were underexplored in Benin's classical bronze casting tradition. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
09515666
Volume :
39
Issue :
1
Database :
Complementary Index
Journal :
AI & Society
Publication Type :
Academic Journal
Accession number :
175388582
Full Text :
https://doi.org/10.1007/s00146-023-01761-7