LayoutGAN for Automated Layout Design in Graphic Design: An Application of Generative Adversarial Networks

Abstract

The current field of visual communication design is particularly influenced by artificial intelligence, with automatic processing applications for images and color schemes having emerged, and a number of design models for intelligent layout have been proposed. However, the statistical learning framework model will face the limitation of black box, the process of graphic design cannot be separated from the real-time participation of designers, so intelligent graphic design is still essentially a semi intelligent system. In this paper, LayoutGAN is used to study the layout generation problem in graphic design. The network structure is designed in accordance with the idea of LayoutGAN. Then experiments are conducted on MINST dataset to verify the effectiveness of the network structure. The experimental results show that the wireframe rendering discriminator is better than the relationship-based discriminator, and then only the LayoutGAN with wireframe rendering discriminator is used to experiment on the room floor plan dataset, and finally the automatic floor plan generation is realized.

References

Kim, J., & Suk, H. J. (2020). Prediction of the emotion responses to poster designs based on graphical features: A machine learning-driven approach. Archives of Design Research, 33(2), 39-55.

Elkhattat, D., & Medhat, M. (2022). Creativity in packaging design as a competitive promotional tool. Information Sciences Letters, 11(1), 135-145.

Wu, Y. (2024). Reference image aided color matching design based on interactive genetic algorithm. Journal of Electrical Systems, 20(2), 400-410.

Feltrin, F., Leccese, F., Hanselaer, P., & Smet, K. A. (2020). Impact of illumination correlated color temperature, background lightness, and painting color content on color appearance and appreciation of paintings. Leukos, 16(1), 25-44.

Chinazzo, G., Chamilothori, K., Wienold, J., & Andersen, M. (2021). Temperature–color interaction: subjective indoor environmental perception and physiological responses in virtual reality. Human factors, 63(3), 474-502.

Ramezani Nia, M., & Shokouhyar, S. (2020). Analyzing the effects of visual aesthetic of Web pages on users’ responses in online retailing using the VisAWI method. Journal of Research in Interactive Marketing, 14(4), 357-389.

Sample, K. L., Hagtvedt, H., & Brasel, S. A. (2020). Components of visual perception in marketing contexts: A conceptual framework and review. Journal of the Academy of Marketing Science, 48(2) 405-421.

Song, J., Brown, M. K., Tan, M., MacGregor, G. A., Webster, J., Campbell, N. R., et al. (2021). Impact of color-coded and warning nutrition labelling schemes: A systematic review and network meta-analysis. PLoS medicine, 18(10), e1003765- e1003777.

Franconeri, S. L., Padilla, L. M., Shah, P., Zacks, J. M., & Hullman, J. (2021). The science of visual data communication: What works. Psychological Science in the public interest, 22(3), 110-161.

Roth, R. E. (2021). Cartographic design as visual storytelling: synthesis and review of map-based narratives, genres, and tropes. The Cartographic Journal, 58(1), 83-114.

Harsanto, P. W., & Jakti, J. W. (2021). The effect of mineral water packaging designs on level of consumer decision in purchase. International Journal of Creative and Arts Studies, 8(2), 161-173.

Bar, A., Gandelsman, Y., Darrell, T., Globerson, A., & Efros, A. (2022). Visual prompting via image inpainting. Advances in Neural Information Processing Systems, 35(3), 25005-25017.

Ren, Z., Fang, F., Yan, N., & Wu, Y. (2022). State of the art in defect detection based on machine vision. International Journal of Precision Engineering and Manufacturing-Green Technology, 9(2), 661-691.

Wang, W., Wu, X., Yuan, X., & Gao, Z. (2020). An experiment-based review of low-light image enhancement methods. IEEE Access, 8(3), 87884-87917.

Li, C., Kwok, L., K. L., Liu, J., & Ye, Q. (2023). Let photos speak: The effect of user-generated visual content on hotel review helpfulness. Journal of Hospitality & Tourism Research, 47(4), 665-690.

Sigut, J., Castro, M., Arnay, R., & Sigut, M. (2020). OpenCV basics: A mobile application to support the teaching of computer vision concepts. IEEE Transactions on Education, 63(4), 328-335.

Wu, Z., Chen, Y., Zhao, B., Kang, X., & Ding, Y. (2021). Review of weed detection methods based on computer vision. Sensors, 21(11), 3647.

Authors

  • Sufen Liu 1Digital media department, Guangzhou Institute of Software, Guangzhou 510990, China 2School of Art, CITl University of Mongolia, Urabato 999097, Mongolia
  • Linzhi Zhou 1Digital media department, Guangzhou Institute of Software, Guangzhou 510990, China 2School of Art, CITl University of Mongolia, Urabato 999097, Mongolia

DOI:

https://doi.org/10.31449/inf.v49i10.7099

Downloads

Published

01/28/2025

How to Cite

Liu, S., & Zhou, L. (2025). LayoutGAN for Automated Layout Design in Graphic Design: An Application of Generative Adversarial Networks. Informatica, 49(10). https://doi.org/10.31449/inf.v49i10.7099

Issue

Vol. 49 No. 10 (2025): Online-only issue

Section

Online-only

License

I assign to Informatica, An International Journal of Computing and Informatics ("Journal") the copyright in the manuscript identified above and any additional material (figures, tables, illustrations, software or other information intended for publication) submitted as part of or as a supplement to the manuscript ("Paper") in all forms and media throughout the world, in all languages, for the full term of copyright, effective when and if the article is accepted for publication. This transfer includes the right to reproduce and/or to distribute the Paper to other journals or digital libraries in electronic and online forms and systems.

I understand that I retain the rights to use the pre-prints, off-prints, accepted manuscript and published journal Paper for personal use, scholarly purposes and internal institutional use.

In certain cases, I can ask for retaining the publishing rights of the Paper. The Journal can permit or deny the request for publishing rights, to which I fully agree.

I declare that the submitted Paper is original, has been written by the stated authors and has not been published elsewhere nor is currently being considered for publication by any other journal and will not be submitted for such review while under review by this Journal.  The Paper contains no material that violates proprietary rights of any other person or entity. I have obtained written permission from copyright owners for any excerpts from copyrighted works that are included and have credited the sources in my article. I have informed the co-author(s) of the terms of this publishing agreement.


Copyright ©  Slovenian Society Informatika