Abstract
Characters are essential elements of games and are critical to their success. At the same time, designing good characters can be time and labor intensive, especially when developing games with thousands of characters. In such cases, procedural generation may be used to expedite the process. However, characters generated by traditional procedural generation techniques often rely on a limited pool of premade assets and may lack novelty. This work explores deep learning for the conditional generation of creative character designs with artist input. It proposes a framework which receives artists’ inputs in the form of blurred character silhouettes and converts these into high resolution character designs using Generative Adversarial Networks. In addition, the paper presents a demo Graphical User Interface and user study evaluating the tool’s effectiveness.
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
References
Pradantyo, R., Birk, M.V., Bateman, S.: How the visual design of video game antagonists affects perception of morality. Front. Comput. Sci. 3 (2021)
Magic: The Gathering. https://magic.wizards.com/en. Accessed 21 Nov 2021
Filho, J., Machado, L., Chiccha, N., Franco, A., Maia, J.: Character design: a new process and its application in a trading card game. In: Proceedings of SBGames. São Paulo (2016)
Freiknecht, J., Effelsberg, W.: A survey on the procedural generation of virtual worlds. Multimodal Technol. Interact. 1(4), 27 (2017)
MakeHuman. https://windows.softwsp.com/makehuman/. Accessed 21 Nov 2021
Goodfellow, I., et al.: Generative Adversarial Nets. Advances in Neural Information Processing Systems, pp. 2672–2680 (2014)
Isola, P., Zhu, J., 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 (2017)
Wang, T., Liu, M., Zhu, J., Tao, A., Kautz, J., Catanzaro, B.: High-resolution image synthesis and semantic manipulation with conditional GANs. In: Proceedings of the IEEE conference on Computer Vision and Pattern Recognition (2018)
Zhu, J., et al.: Toward multimodal image-to-image translation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2017)
Park, T., Liu, M., Wang, T., Zhu, J.: semantic image synthesis with spatially-adaptive normalization. In: Proceedings of the IEEE conference on Computer Vision and Pattern Recognition (2019)
Karras, T., Laine, S., Aila, T.: A style-based generator architecture for generative adversarial networks. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2019)
Karras, T., Laine, S., Aittala, M., Hellsten, J., Lehtinen, J., Aila, T.: Analyzing and Improving the Image Quality of StyleGAN. arXiv:1912.04958 [cs.CV] (2019)
Elad, R., et al.: Encoding in style: a StyleGAN encoder for image to image translation. In: Proceedings of the IEEE conference on Computer Vision and Pattern Recognition, pp. 2287–2296 (2021)
Shen, Y., Zhou, B.: Closed-form factorization of latent semantics in GANs. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2021)
Härkönen, E., Hertzmann, A., Lehtinen, J., Paris, S.: GANSpace: discovering interpretable GAN controls. arXiv:2004.02546 [cs.CV] (2020)
Brock, A., Donahue, J., Simonyan, K.: Large scale GAN training for high fidelity natural image synthesis. arXiv:1809.11096 [cs.CV] (2018)
This Person Does Not Exist. https://thispersondoesnotexist.com/. Accessed 21 Nov 2021
Simon, J.: ArtBreeder. https://www.artbreeder.com/. Accessed 21 Nov 2021
Toor, A.S., Bertsch, F.: Using GANs to create fantastical creatures. Google AI Blog. https://ai.googleblog.com/2020/11/using-gans-to-create-fantastical.html/. Accessed 21 Nov 2021
DuckDuckGo. https://duckduckgo.com/. Accessed 21 Nov 2021
Zepeng, L.: Generating Full-Body Standing Figures of Anime Characters and Its Style Transfer by GAN. Graduate School of Fundamental Science and Engineering, Waseda University, Thesis (2020)
Breheret, A.: Github repository. https://github.com/abreheret/PixelAnnotationTool. Accessed 21 Nov 2021
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 IFIP International Federation for Information Processing
About this paper
Cite this paper
Guo, L., Bhojan, A. (2022). Converting Nebulous Ideas to Reality – A Deep Learning Tool for Conditional Synthesis of Character Designs. In: Neuhold, E.J., Fernando, X., Lu, J., Piramuthu, S., Chandrabose, A. (eds) Computer, Communication, and Signal Processing. ICCCSP 2022. IFIP Advances in Information and Communication Technology, vol 651. Springer, Cham. https://doi.org/10.1007/978-3-031-11633-9_7
Download citation
DOI: https://doi.org/10.1007/978-3-031-11633-9_7
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-11632-2
Online ISBN: 978-3-031-11633-9
eBook Packages: Computer ScienceComputer Science (R0)
