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Lookup NU author(s): Dr Lei ShiORCiD
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Layout guides the position and scale of design elements for desirable aesthetics and effective demonstration. Recently, Generative Adversarial Networks (GANs) have proved their capability in generating effective layouts. However, current GANs ignore the situation where the amounts and types of the input design elements are given and determined. In this paper, we propose EcGAN, an element-conditioned GAN for graphic layout generation conditioned on specified design elements (design elements’ amount and types). We represent each element by a bounding box and propose three components: element mask, element condition loss and two-step discriminators, to solve the bounding box modelling problem for element-conditioned layout generation. Experiments reveal that EcGAN outperforms existing methods quantitatively and qualitatively. We also perform detailed ablation studies to highlight the effect of each component and a user study to further validate our model. Finally, we demonstrate two of EcGAN’s applications for practical design scenarios.
Author(s): Chen L, Jing Q, Zhou Y, Li Z, Shi L, Sun L
Publication type: Article
Publication status: Published
Journal: Neurocomputing
Year: 2024
Volume: 591
Print publication date: 28/07/2024
Online publication date: 23/04/2024
Acceptance date: 20/04/2024
ISSN (print): 0925-2312
ISSN (electronic): 1872-8286
Publisher: Elsevier
URL: https://doi.org/10.1016/j.neucom.2024.127730
DOI: 10.1016/j.neucom.2024.127730
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