GANILLA: Generative Adversarial Networks for Image to Illustration Translation

• URL: http://arxiv.org/abs/2002.05638v2
• Date: Fri, 14 Feb 2020 09:46:35 GMT
• Title: GANILLA: Generative Adversarial Networks for Image to Illustration Translation
• Authors: Samet Hicsonmez, Nermin Samet, Emre Akbas, Pinar Duygulu
• Abstract summary: 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.
• Score: 12.55972766570669
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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 image-to-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-to-illustration 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.

Related papers

• Removing Distributional Discrepancies in Captions Improves Image-Text Alignment [76.31530836622694]
  We introduce a model designed to improve the prediction of image-text alignment. Our approach focuses on generating high-quality training datasets for the alignment task. We also demonstrate the applicability of our model by ranking the images generated by text-to-image models based on text alignment.
  arXiv  Detail & Related papers  (2024-10-01T17:50:17Z)
• Unsupervised Image-to-Image Translation with Generative Prior [103.54337984566877]
  Unsupervised image-to-image translation aims to learn the translation between two visual domains without paired data. We present a novel framework, Generative Prior-guided UN Image-to-image Translation (GP-UNIT), to improve the overall quality and applicability of the translation algorithm.
  arXiv  Detail & Related papers  (2022-04-07T17:59:23Z)
• Zero-Shot Image-to-Text Generation for Visual-Semantic Arithmetic [72.60554897161948]
  Recent text-to-image matching models apply contrastive learning to large corpora of uncurated pairs of images and sentences. In this work, we repurpose such models to generate a descriptive text given an image at inference time. The resulting captions are much less restrictive than those obtained by supervised captioning methods.
  arXiv  Detail & Related papers  (2021-11-29T11:01:49Z)
• Fine-Tuning StyleGAN2 For Cartoon Face Generation [0.0]
  We propose a novel image-to-image translation method that generates images of the target domain by finetuning a stylegan2 pretrained model. The stylegan2 model is suitable for unsupervised I2I translation on unbalanced datasets.
  arXiv  Detail & Related papers  (2021-06-22T14:00:10Z)
• toon2real: Translating Cartoon Images to Realistic Images [1.4419517737536707]
  We apply several state-of-the-art models to perform this task; however, they fail to perform good quality translations. We propose a method based on CycleGAN model for image translation from cartoon domain to photo-realistic domain. 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  Detail & Related papers  (2021-02-01T20:22:05Z)
• Unsupervised Image-to-Image Translation via Pre-trained StyleGAN2 Network [73.5062435623908]
  We propose a new I2I translation method that generates a new model in the target domain via a series of model transformations. By feeding the latent vector into the generated model, we can perform I2I translation between the source domain and target domain.
  arXiv  Detail & Related papers  (2020-10-12T13:51:40Z)
• Retrieval Guided Unsupervised Multi-domain Image-to-Image Translation [59.73535607392732]
  Image to image translation aims to learn a mapping that transforms an image from one visual domain to another. We propose the use of an image retrieval system to assist the image-to-image translation task.
  arXiv  Detail & Related papers  (2020-08-11T20:11:53Z)
• COCO-FUNIT: Few-Shot Unsupervised Image Translation with a Content Conditioned Style Encoder [70.23358875904891]
  Unsupervised image-to-image translation aims to learn a mapping of an image in a given domain to an analogous image in a different domain. We propose a new few-shot image translation model, COCO-FUNIT, which computes the style embedding of the example images conditioned on the input image. Our model shows effectiveness in addressing the content loss problem.
  arXiv  Detail & Related papers  (2020-07-15T02:01:14Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.