Neural Style Transfer: A Paradigm Shift for Image-based Artistic Rendering?
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Date
2017
Journal Title
Journal ISSN
Volume Title
Publisher
Association for Computing Machinery, Inc (ACM)
Abstract
In 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.
Description
@inproceedings{10.1145:3092919.3092920,
booktitle = {Non-Photorealistic Animation and Rendering},
editor = {Holger Winnemoeller and Lyn Bartram},
title = {{Neural Style Transfer: A Paradigm Shift for Image-based Artistic Rendering?}},
author = {Semmo, Amir and Isenberg, Tobias and Döllner, Jürgen},
year = {2017},
publisher = {Association for Computing Machinery, Inc (ACM)},
ISSN = {-},
ISBN = {978-1-4503-5081-5},
DOI = {10.1145/3092919.3092920}
}