Generative Adversarial Networks for photo to Hayao Miyazaki style cartoons
This work addresses style transfer for cartoon enthusiasts and artists, but it is incremental as it builds on existing CartoonGAN methods.
The paper tackled the problem of transferring Hayao Miyazaki's cartoon style to real-life photos using a Generative Adversarial Network trained on over 60,000 images, and a qualitative survey of 117 results showed that their model outranked state-of-the-art methods in cartoon-likeness.
This paper takes on the problem of transferring the style of cartoon images to real-life photographic images by implementing previous work done by CartoonGAN. We trained a Generative Adversial Network(GAN) on over 60 000 images from works by Hayao Miyazaki at Studio Ghibli. To evaluate our results, we conducted a qualitative survey comparing our results with two state-of-the-art methods. 117 survey results indicated that our model on average outranked state-of-the-art methods on cartoon-likeness.