Cartoon-to-real: An Approach to Translate Cartoon to Realistic Images using GAN

Autor:	Sultan, K M Arefeen, Rupty, Labiba Kanij, Pranto, Nahidul Islam, Shuvo, Sayed Khan, Jubair, Mohammad Imrul
Rok vydání:	2018
Předmět:	Computer Science - Computer Vision and Pattern Recognition
Druh dokumentu:	Working Paper
Popis:	We propose a method to translate cartoon images to real world images using Generative Aderserial Network (GAN). Existing GAN-based image-to-image translation methods which are trained on paired datasets are impractical as the data is difficult to accumulate. Therefore, in this paper we exploit the Cycle-Consistent Adversarial Networks (CycleGAN) method for images translation which needs an unpaired dataset. By applying CycleGAN we show that our model is able to generate meaningful real world images from cartoon images. However, we implement another state of the art technique $-$ Deep Analogy $-$ to compare the performance of our approach. Comment: This is an ongoing work and this draft contains the future plan to accomplish the tasks
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/1811.11796 Zobrazit plný text záznamu View this record from Arxiv