Abstract
We propose a methodology for generating creative cartoon art. The system generates cartoon by looking at various existing images of cartoon characters and learning about their posture/animation style. The proposed system is creative in nature as it generates unique cartoon art by deviating from the existing styles learned by the algorithm. We build over Generative Adversarial Networks (GAN) with unsupervised learning, which have shown the ability to learn to generate novel cartoons by simulating a given distribution. The proposed model exhibits an ability to generate cartoons which are creative and novel in design. We have conducted experiments by considering around 12K Tom’s cartoon images for training purposes. The results show that the increase in number of epochs resulted in better classification accuracy. The proposed system generates the character Tom’s cartoons which are novel and we have validated the same by applying Colton’s creativity benchmark.
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Gawade, A., Pandharkar, R., Deolekar, S. (2020). GANTOON: Creative Cartoons Using Generative Adversarial Network. In: Badica, C., Liatsis, P., Kharb, L., Chahal, D. (eds) Information, Communication and Computing Technology. ICICCT 2020. Communications in Computer and Information Science, vol 1170. Springer, Singapore. https://doi.org/10.1007/978-981-15-9671-1_19
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DOI: https://doi.org/10.1007/978-981-15-9671-1_19
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