Abstract
The work by Gatys et al. [1] recently showed a neural style algorithm that can produce an image in the style of another image. Some further works introduced various improvements regarding generalization, quality and efficiency, but each of them was mostly focused on styles such as paintings, abstract images or photo-realistic style. In this paper, we present a comparison of how state-of-the-art style transfer methods cope with transferring various comic styles on different images. We select different combinations of Adaptive Instance Normalization [11] and Universal Style Transfer [16] models and confront them to find their advantages and disadvantages in terms of qualitative and quantitative analysis. Finally, we present the results of a survey conducted on over 100 people that aims at validating the evaluation results in a real-life application of comic style transfer.
Abstract (translated)
Gatys等人的工作。 [1]最近展示了一种神经风格算法，可以产生另一种图像风格的图像。一些进一步的作品介绍了关于泛化，质量和效率的各种改进，但是每一项都主要集中在绘画，抽象图像或照片写实风格等风格上。在本文中，我们比较了最先进的风格转换方法如何应对在不同图像上转换各种漫画风格。我们选择自适应实例标准化[11]和通用样式转移[16]模型的不同组合，并与它们对比，以找出它们在定性和定量分析方面的优缺点。最后，我们介绍了对超过100人进行的调查结果，旨在验证漫画风格转移的实际应用中的评估结果。
URL
https://arxiv.org/abs/1809.01726
