Abstract
Recent advances in Generative Adversarial Networks (GANs) continue to attract the attention of researchers in different fields due to the wide range of applications devised to take advantage of their key features. Most recent GANs are focused on realism; however, generating hyper-realistic output is not a priority for some domains, as in the case of this work. The generated outcomes are used here as cognitive components to augment character designers’ creativity while conceptualizing new characters for different multimedia projects. To select the best-suited GANs for such a creative context, we first present a comparison between different GAN architectures and their performance when trained from scratch on a new visual character’s dataset using a single Graphics Processing Unit (GPU). We also explore alternative techniques, such as transfer learning and data augmentation, to overcome computational resource limitations, a challenge faced by many researchers in the domain. Additionally, mixed methods are used to evaluate the cognitive value of the generated visuals on character designers’ agency conceptualizing new characters. The results discussed proved highly effective for this context, as demonstrated by early adaptations to the characters’ design process. As an extension for this work, the presented approach will be further evaluated as a novel co-design process between humans and machines to investigate where and how the generated concepts are interacting with and influencing the design process outcome.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Goodfellow, I., et al.: Generative adversarial nets, 27, 2672–2680 (2014)
Borji, A.: Generated faces in the wild: quantitative comparison of stable diffusion, midjourney and dall-e 2, arXiv preprint arXiv:2210.00586 (2022)
Heusel, M., Ramsauer, H., Unterthiner, T., Nessler, B., Hochreiter, S.: GANs trained by a two time-scale update rule converge to a local nash equilibrium. In: Guyon, I., et al. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc. (2017)
Krizhevsky, A., Nair, V., Hinton, G.: Cifar-10 (Canadian Institute for Advanced Research). http://www.cs.toronto.edu/kriz/cifar.html
Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: a large-scale hierarchical image database. In: 2009 IEEE Conference on Computer Vision and Pattern Recognition, pp. 248–255. IEEE (2009)
Yu, F., Zhang, Y., Song, S., Seff, A., Xiao, J.: LSUN: construction of a large-scale image dataset using deep learning with humans in the loop. CoRR, abs/1506.03365 (2015). http://arxiv.org/abs/1506.03365
Karras, T., Aila, T., Laine, S., Lehtinen, J.: Progressive growing of GANs for improved quality, stability, and variation (2018)
Tero Karras, T.A., Laine, S.: Flickr-faces-HQ dataset (FFHQ) (2018). https://github.com/nvlabs/ffhq-dataset
Karras, T., Aittala, M., Hellsten, J., Laine, S., Lehtinen, J., Aila, T.: Training generative adversarial networks with limited data. In: Larochelle, H., Ranzato, M., Hadsell, R., Balcan, M.F., Lin, H. (eds.) Advances in Neural Information Processing Systems, vol. 33, pp. 12:104–12:114. Curran Associates, Inc.(2020)
Tan, C., Sun, F., Kong, T., Zhang, W., Yang, C., Liu, C.: A survey on deep transfer learning. In: Kůrková, V., Manolopoulos, Y., Hammer, B., Iliadis, L., Maglogiannis, I. (eds.) ICANN 2018. LNCS, vol. 11141, pp. 270–279. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01424-7_27
Fregier, Y., Gouray, J.-B.: Mind2mind: transfer learning for GANs. In: Nielsen, F., Barbaresco, F. (eds.) Geometric Science of Information, pp. 851–859. Springer, Cham (2021)
Rombach, R., Blattmann, A., Lorenz, D., Esser, P., Ommer, B.: High-resolution image synthesis with latent diffusion models (2021)
Park, T., Liu, M.-Y., Wang, T.-C., Zhu, J.-Y.: Semantic image synthesis with spatially-adaptive normalization. In: 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, June 2019. https://doi.org/10.1109/CVPR.2019.00244
Weatherbed, J.: Artstation is hiding images protesting ai art on the platform, December 2022
Baio, A.: Invasive diffusion: how one unwilling illustrator found herself turned into an ai model, November 2022
Growcoot, M.: Lawsuit filed against ai image generators stable diffusion and midjourney, January 2023
Fish, J., Scrivener, S.: Amplifying the mind’s eye: sketching and visual cognition. Leonardo 23, 117–126 (1990)
Goldschmidt, G.: The dialectics of sketching. Creat. Res. J. 4, 123–143 (1991)
LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition (1998)
Theis, L., van den Oord, A., Bethge, M.: A note on the evaluation of generative models, November 2015
Arjovsky, M., Chintala, S., Bottou, L.: Wasserstein generative adversarial networks. In: International Conference on Machine Learning, pp. 214–223. PMLR (2017)
Gulrajani, I., Ahmed, F., Arjovsky, M., Dumoulin, V., Courville, A.C.: Improved training of wasserstein GANs. In: Guyon, I., et al. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc. (2017)
Mahdizadehaghdam, S., Panahi, A., Krim, H.: Sparse generative adversarial network. In: Proceedings - 2019 International Conference on Computer Vision Workshop, ICCVW 2019, pp. 3063–3071 (2019)
Hong, Y., Hwang, U., Yoo, J., Yoon, S.: How generative adversarial networks and their variants work: an overview, 52(1), 1–43 (2019)
Creswell, A., White, T., Dumoulin, V., Arulkumaran, K., Sengupta, B., Bharath, A.A.: Generative adversarial networks: an overview, 35(1), 53–65 (2018)
Brock, A., Donahue, J., Simonyan, K.: Large scale GAN training for high fidelity natural image synthesis. In: International Conference on Learning Representations (2019)
Hinton, G., Vinyals, O., Dean, J.: Distilling the knowledge in a neural network (2015). https://arxiv.org/abs/1503.02531
Karras, T., Aila, T., Laine, S., Lehtinen, J.: Progressive growing of GANs for improved quality, stability, and variation. abs/1710.10196 (2017)
Karras, T., Laine, S., Aila, T.: A style-based generator architecture for generative adversarial networks. In: 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 4396–4405 (2019)
Karras, T., Laine, S., Aittala, M., Hellsten, J., Lehtinen, J., Aila, T.: Analyzing and improving the image quality of stylegan. In: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 8107–8116 (2020)
Abdal, R., Qin, Y., Wonka, P.: Image2StyleGAN: how to embed images into the StyleGAN latent space?, vol. Oct, pp. 4431–4440 (2019)
Lataifeh, M., Carrasco, X., Elnagar, A.: Diversified character dataset for creative applications (DCDCA) (2022)
Isola, P., Zhu, J.-Y., Zhou, T., Efros, A.A.: Image-to-image translation with conditional adversarial networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1125–1134 (2017)
Dreyfus, H.L., Dreyfus, S.E.: Peripheral vision: expertise in real world contexts. Organ. Stud. 26(5), 779–792 (2005)
Payne, J.W.: Thinking aloud: insights into information processing, 5(5), 241–248 (1994)
Sadowska, N., Laffy, D.: The design brief: inquiry into the starting point in a learning journey. Des. J. 20, S1380–S1389 (2017)
Brittain, B.: AI-created images lose U.S. copyrights in test for new technology. Reuters (2023). https://www.reuters.com/legal/ai-created-images-lose-us-copyrights-test-new-technology. Accessed 22 Feb 2023
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Lataifeh, M., Carrasco, X., Elnagar, A., Ahmed, N. (2023). Augmenting Character Designers’ Creativity Using Generative Adversarial Networks. In: Daimi, K., Al Sadoon, A. (eds) Proceedings of the Second International Conference on Innovations in Computing Research (ICR’23). Lecture Notes in Networks and Systems, vol 721. Springer, Cham. https://doi.org/10.1007/978-3-031-35308-6_7
Download citation
DOI: https://doi.org/10.1007/978-3-031-35308-6_7
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-35307-9
Online ISBN: 978-3-031-35308-6
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)