Learning-based Artificial Intelligence Artwork
Methodology Taxonomy and Quality Evaluation
Qian Wang (Guangdong University of Technology)
Hong Ning Dai (Hong Kong Baptist University)
Jinghua Yang (Southwest Jiaotong University)
Cai Guo (Hanshan Normal University, Chaozhou)
Peter Childs (Imperial College London)
Maaike Kleinsmann (TU Delft - Design, Organisation and Strategy)
Yike Guo (The Hong Kong University of Science and Technology)
Pan Wang (TU Delft - Creative Processes)
More Info
expand_more
Other than for strictly personal use, it is not permitted to download, forward or distribute the text or part of it, without the consent of the author(s) and/or copyright holder(s), unless the work is under an open content license such as Creative Commons.
Abstract
With the development of the theory and technology of computer science, machine or computer painting is increasingly being explored in the creation of art. Machine-made works are referred to as artificial intelligence (AI) artworks. Early methods of AI artwork generation have been classified as non-photorealistic rendering, and, latterly, neural style transfer methods have also been investigated. As technology advances, the variety of machine-generated artworks and the methods used to create them have proliferated. However, there is no unified and comprehensive system to classify and evaluate these works. To date, no work has generalized methods of creating AI artwork including learning-based methods for painting or drawing. Moreover, the taxonomy, evaluation, and development of AI artwork methods face many challenges. This article is motivated by these considerations. We first investigate current learning-based methods for making AI artworks and classify the methods according to art styles. Furthermore, we propose a consistent evaluation system for AI artworks and conduct a user study to evaluate the proposed system on different AI artworks. This evaluation system uses six criteria: beauty, color, texture, content detail, line, and style. The user study demonstrates that the six-dimensional evaluation index is effective for different types of AI artworks.
help_outline