CartoonGAN: Generative Adversarial Networks for Photo Cartoonization

Yang, Chen; Lai, Yu‐Kun; Liu, Yong-Jin

doi:10.1109/cvpr.2018.00986

Cited by 327 publications

(267 citation statements)

References 28 publications

(46 reference statements)

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“…We compared our method with three state-of-the-art GAN methods that use unpaired data: CartoonGAN [5], CycleGAN [3] and DualGAN [4]. We used their official implementations that are publicly available.…”

Section: Discussionmentioning

confidence: 99%

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GANILLA: Generative adversarial networks for image to illustration translation

Hiçsönmez

Samet

Akbaş

et al. 2020

Image and Vision Computing

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In this paper, we explore illustrations in children's books as a new domain in unpaired image-to-image translation. We show that although the current state-of-the-art image-to-image translation models successfully transfer either the style or the content, they fail to transfer both at the same time. We propose a new generator network to address this issue and show that the resulting network strikes a better balance between style and content.There are no well-defined or agreed-upon evaluation metrics for unpaired imageto-image translation. So far, the success of image translation models has been based on subjective, qualitative visual comparison on a limited number of images. To address this problem, we propose a new framework for the quantitative evaluation of image-toillustration models, where both content and style are taken into account using separate classifiers. In this new evaluation framework, our proposed model performs better than the current state-of-the-art models on the illustrations dataset. Our code and pretrained models can be found at https://github.com/giddyyupp/ganilla.

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Section: Discussionmentioning

confidence: 99%

“…Collecting such datasets requires too much effort. To overcome this disadvantage, the second group requires unpaired image sets [3,4,5,13]. These methods use two separate image collections, i.e.…”

Section: Related Workmentioning

confidence: 99%

GANILLA: Generative adversarial networks for image to illustration translation

Hiçsönmez

Samet

Akbaş

et al. 2020

Image and Vision Computing

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show abstract

“…AdaIN [6] successfully generates images with smooth colors but suffers from serious artifacts. CartoonGAN [3] produces clear images without artifacts, but the generated results are too close to the input photos and the color distribution is very monotonous. In other words, the extent of cartoonization with CartoonGAN [3] is not enough.…”

Section: Comparison With State Of the Artmentioning

confidence: 95%

“…CartoonGAN [3] produces clear images without artifacts, but the generated results are too close to the input photos and the color distribution is very monotonous. In other words, the extent of cartoonization with CartoonGAN [3] is not enough. In contrast, our method apparently produces higher-quality cartoonized images, which have high contrast between colors and contains very clear edges.…”

Section: Comparison With State Of the Artmentioning

confidence: 95%

CartoonRenderer: An Instance-Based Multi-style Cartoon Image Translator

Chen

Song

et al. 2019

MultiMedia Modeling

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Instance based photo cartoonization is one of the challenging image stylization tasks which aim at transforming realistic photos into cartoon style images while preserving the semantic contents of the photos. State-of-the-art Deep Neural Networks (DNNs) methods still fail to produce satisfactory results with input photos in the wild, especially for photos which have high contrast and full of rich textures. This is due to that: cartoon style images tend to have smooth color regions and emphasized edges which are contradict to realistic photos which require clear semantic contents, i.e., textures, shapes etc. Previous methods have difficulty in satisfying cartoon style textures and preserving semantic contents at the same time. In this work, we propose a novel "Cartoon-Renderer" framework which utilizing a single trained model to generate multiple cartoon styles. In a nutshell, our method maps photo into a feature model and renders the feature model back into image space. In particular, cartoonization is achieved by conducting some transformation manipulation in the feature space with our proposed Soft-AdaIN. Extensive experimental results show our method produces higher quality cartoon style images than prior arts, with accurate semantic content preservation. In addition, due to the decoupling of whole generating process into "Modeling-Coordinating-Rendering" parts, our method could easily process higher resolution photos, which is intractable for existing methods.

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“…On the other hand, the cartoon style is more simplified and abstract than the realistic style, so that such cartoon sketch can improve visual consistency of the storyboard. In this work, we utilize a pretrained CartoonGAN [32] for the cartoon style transfer.…”

Section: Style Unificationmentioning

confidence: 99%

Neural Storyboard Artist

Chen

Liu

et al. 2019

Proceedings of the 27th ACM International Conference on Multimedia

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A storyboard is a sequence of images to illustrate a story containing multiple sentences, which has been a key process to create different story products. In this paper, we tackle a new multimedia task of automatic storyboard creation to facilitate this process and inspire human artists. Inspired by the fact that our understanding of languages is based on our past experience, we propose a novel inspire-and-create framework with a story-to-image retriever that selects relevant cinematic images for inspiration and a storyboard creator that further refines and renders images to improve the relevancy and visual consistency. The proposed retriever dynamically employs contextual information in the story with hierarchical attentions and applies dense visual-semantic matching to accurately retrieve and ground images. The creator then employs three rendering steps to increase the flexibility of retrieved images, which include erasing irrelevant regions, unifying styles of images and substituting consistent characters. We carry out extensive experiments on both in-domain and out-of-domain visual story datasets. The proposed model achieves better quantitative performance than the state-of-the-art baselines for storyboard creation. Qualitative visualizations and user studies further verify that our approach can create high-quality storyboards even for stories in the wild.

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CartoonGAN: Generative Adversarial Networks for Photo Cartoonization

Cited by 327 publications

References 28 publications

GANILLA: Generative adversarial networks for image to illustration translation

GANILLA: Generative adversarial networks for image to illustration translation

CartoonRenderer: An Instance-Based Multi-style Cartoon Image Translator

Neural Storyboard Artist

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