Neural Comic Style Transfer: Case Study
This work addresses the problem of comic style transfer for image processing applications, but it is incremental as it focuses on comparing existing methods rather than introducing new ones.
The paper compared state-of-the-art style transfer methods for applying comic styles to images, conducting qualitative and quantitative analyses and validating results with a survey of over 100 people.
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.