toon2real: Translating Cartoon Images to Realistic Images

Abstract

In terms of Image-to-image translation, Generative Adversarial Networks (GANs) has achieved great success even when it is used in the unsupervised dataset. In this work, we aim to translate cartoon images to photo-realistic images using GAN. We apply several state-of-the-art models to perform this task; however, they fail to perform good quality translations. We observe that the shallow difference between these two domains causes this issue. Based on this idea, we propose a method based on CycleGAN model for image translation from cartoon domain to photo-realistic domain. To make our model efficient, we implemented Spectral Normalization which added stability in our model. We demonstrate our experimental results and show that our proposed model has achieved the lowest Frechet Inception Distance score and better results compared to another state-of-the-art technique, UNIT.

arXiv commentAccepted as a short paper at ICTAI 2020

References in corpus (14)

• Auto-Encoding Variational Bayes
• Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
• Deep Residual Learning for Image Recognition
• Spectral Normalization for Generative Adversarial Networks
• Generative Adversarial Text to Image Synthesis
• Progressive Growing of GANs for Improved Quality, Stability, and Variation
• Improved Training of Wasserstein GANs
• Weight Normalization: A Simple Reparameterization to Accelerate Training of Deep Neural Networks
• Learning to Discover Cross-Domain Relations with Generative Adversarial Networks
• Autoencoding beyond pixels using a learned similarity metric
• Dual Learning for Machine Translation
• Unsupervised Image-to-Image Translation Networks
• SingleGAN: Image-to-Image Translation by a Single-Generator Network using Multiple Generative Adversarial Learning
• Cartoon-to-real: An Approach to Translate Cartoon to Realistic Images using GAN