TY - GEN
T1 - Axial Generation
T2 - 10th International Conference on Artificial Intelligence in Music, Sound, Art and Design, EvoMUSART 2021 held as Part of EvoStar 2021
AU - Easton, Edward
AU - Ekárt, Anikó
AU - Bernardet, Ulysses
PY - 2021/4/2
Y1 - 2021/4/2
N2 - 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.
AB - 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.
KW - 2D and 3D art generation
KW - Concretism
KW - Evolutionary computation
UR - http://www.scopus.com/inward/record.url?scp=85107426433&partnerID=8YFLogxK
UR - https://link.springer.com/chapter/10.1007%2F978-3-030-72914-1_8
U2 - 10.1007/978-3-030-72914-1_8
DO - 10.1007/978-3-030-72914-1_8
M3 - Conference publication
AN - SCOPUS:85107426433
SN - 9783030729134
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 115
EP - 130
BT - Artificial Intelligence in Music, Sound, Art and Design - 10th International Conference, EvoMUSART 2021, Held as Part of EvoStar 2021, Proceedings
A2 - Romero, Juan
A2 - Martins, Tiago
A2 - Rodríguez-Fernández, Nereida
PB - Springer
Y2 - 7 April 2021 through 9 April 2021
ER -