  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.
