Semmo, AmirIsenberg, TobiasDöllner, JürgenHolger Winnemoeller and Lyn Bartram2017-10-182017-10-182017978-1-4503-5081-5-https://doi.org/10.1145/3092919.3092920https://diglib.eg.org:443/handle/10.2312/npar2017a05In this meta paper we discussimage-based artistic rendering (IB-AR)based onneural style transfer(NST) and argue, while NST may represent a paradigm shift for IB-AR, that it also has to evolve as an interactive tool that considers the design aspects and mecha- nisms of artwork production. IB-AR received signifficant attention in the past decades for visual communication, covering a plethora of techniques to mimic the appeal of artistic media. Example-based renderingrepresents one the most promising paradigms in IB-AR to (semi-)automatically simulate artistic media with high fidelity, but so far has been limited because it relies on pre-defined image pairs for training or informs only low-level image features for texture transfers. Advancements in deep learning showed to alleviate these limitations by matching content and style statistics via activations of neural network layers, thus making a generalized style trans- fer practicable. We categorize style transfers within the taxonomy of IB-AR, then propose a semiotic structure to derive a technical research agenda for NSTs with respect to the grand challenges of NPAR. We finally discuss the potentials of NSTs, thereby identifying applications such as casual creativity and art production.Computing methodologiesNon photorealistic renderingImage processingstyle transferstylizationconvolutional neural networksimagebased artistic renderingimage processingsemiotics10.1145/3092919.3092920