TY - GEN
T1 - GAN-Assisted YUV Pixel Art Generation
AU - Jiang, Zhouyang
AU - Sweetser, Penny
N1 - Publisher Copyright:
© 2022, Springer Nature Switzerland AG.
PY - 2022
Y1 - 2022
N2 - Procedural Content Generation (PCG) in games has grown in popularity in recent years, with Generative Adversarial Networks (GANs) providing a promising option for applying PCG for game artistic asset generation. In this paper, we introduce a model that uses GANs and the YUV colour encoding system for automatic colouring of game assets. In this model, conditional GANs in Pix2Pix architecture are chosen as the main structure and the YUV colour encoding system is used for data preprocessing and result visualisation. We experimented with parameter settings (number of epochs, activation functions, optimisers) to optimise output. Our experimental results show that the proposed model can generate evenly coloured outputs for both small and larger datasets.
AB - Procedural Content Generation (PCG) in games has grown in popularity in recent years, with Generative Adversarial Networks (GANs) providing a promising option for applying PCG for game artistic asset generation. In this paper, we introduce a model that uses GANs and the YUV colour encoding system for automatic colouring of game assets. In this model, conditional GANs in Pix2Pix architecture are chosen as the main structure and the YUV colour encoding system is used for data preprocessing and result visualisation. We experimented with parameter settings (number of epochs, activation functions, optimisers) to optimise output. Our experimental results show that the proposed model can generate evenly coloured outputs for both small and larger datasets.
KW - Art generation
KW - Generative Adversarial Networks
KW - Pixel art
KW - Procedural Content Generation
KW - Video games
UR - http://www.scopus.com/inward/record.url?scp=85127209468&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-97546-3_48
DO - 10.1007/978-3-030-97546-3_48
M3 - Conference contribution
SN - 9783030975456
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 595
EP - 606
BT - AI 2021
A2 - Long, Guodong
A2 - Yu, Xinghuo
A2 - Wang, Sen
PB - Springer Science and Business Media Deutschland GmbH
T2 - 34th Australasian Joint Conference on Artificial Intelligence, AI 2021
Y2 - 2 February 2022 through 4 February 2022
ER -