“CómicGAN: Generación De Ilustraciones Con Redes GAN De Crecimiento Progresivo”, 2021-04-01 (; similar):
This degree work shows the implementation of generative adversarial networks (GANs) to generate completely new illustrations, making use of comic images that have been previously studied, normalized, and filtered. The report will reflect the evolution of the research, which takes as a starting point the results of a previous research in which the first steps to follow in order to obtain a valid dataset are proposed.
The set of steps to obtain the final result are presented: the work methodology used, the remote work configuration using Google Colab, the search and study of architectures and the evolution and improvement of the different networks to achieve the final results.
To carry out the generation of comic illustrations, a set of models is implemented progressively, taking an evolution from simpler to more complex and actual models, in order to better assimilate the necessary knowledge. In this way, the use of progressively growing generative adversarial networks or ProGANs is ultimately proposed. By using the ProGAN architecture, it is possible to improve the results compared to the use of traditional methods, obtaining images of great similarity to the original ones, maintaining the originality and avoiding the direct copy of images from the dataset.
In order to validate and demonstrate that the results obtained are replicable, a comparison between 2 different sets of images has been performed. Although both datasets have the same drawing style, they present substantial differences in their composition. On the one hand, results are shown for images of characters with the whole body and later using illustrations of character faces in the foreground.