toon2real: Translating Cartoon Images to Realistic Images
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Abstract (arXiv)
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.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 23 of its 26 references that are in the map. First posted on arXiv 1 Feb 2021.
Builds on
23 of 26 references.
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11 more
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