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

arXiv: https://arxiv.org/abs/1811.11796 · HTML: https://mlsanity.com/paper/1811.11796/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Nov 2018.

## Built on by (most cited first, 1 of 1)

- [toon2real: Translating Cartoon Images to Realistic Images](https://mlsanity.com/paper/2102.01143/) (2021)

## Abstract (arXiv)

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.