Learning-Based Artificial Intelligence Artwork: Methodology Taxonomy and Quality Evaluation

被引:0
|
作者
Wang, Qian [1 ]
Dai, Hong-ning [2 ]
Yang, Jinghua [3 ]
Guo, Cai [4 ]
Childs, Peter [5 ]
Kleinsmann, Maaike [6 ]
Guo, Yike [7 ]
Wang, Pan [6 ]
机构
[1] Guangdong Univ Technol, Guangzhou, Guangdong, Peoples R China
[2] Hong Kong Baptist Univ, Hong Kong, Kowloon Tong, Peoples R China
[3] Southwest Jiaotong Univ, Chengdu, Sichuan, Peoples R China
[4] HanShan Normal Univ, Chaozhou, Guangdong, Peoples R China
[5] Imperial Coll London, London, England
[6] Delft Univ Technol, Delft, Zuid Holland, Netherlands
[7] Hong Kong Univ Sci & Technol, Clear Water Bay, Hong Kong, Peoples R China
关键词
AI art; artwork; style transform; painting; methodology taxonomy; quality evaluation; GENERATIVE ADVERSARIAL NETWORKS; STYLE TRANSFER; STROKE; ART; MODELS;
D O I
10.1145/3698105
中图分类号
TP301 [理论、方法];
学科分类号
081202 ;
摘要
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.
引用
收藏
页数:37
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