School of Design, Shanghai Jiao Tong University, Shanghai, China.
PLoS One. 2023 Sep 21;18(9):e0291647. doi: 10.1371/journal.pone.0291647. eCollection 2023.
Several automated aesthetic assessment models were developed to assist artists in producing artwork with high aesthetic appeal. However, most of them focused on static visual art, such as photographs and paintings, and evaluations of dynamic art received less attention. Dynamic visual art, especially computer-based art, includes diverse forms of artistic expression and can enhance an audience's aesthetic experience. A model for evaluating dynamic visual art can provide valuable feedback and guidance for improving artistic skills and creativity, thereby benefiting audiences. In this study, we created eight generative artworks with dynamic art forms based on a commonly used method. We established a time-dependent model to predict the aesthetics based on visual features. We quantified the artworks according to selected image features and found that these features could effectively capture the characteristics of the changing visual forms during the generation process. To explore the effects of time-varying features on aesthetic appeal, we built a panel regression model and found that the aesthetic appeal of the generated artworks was significantly affected by their skewness of the luminance distribution, vertical symmetry, and mean hue value. Furthermore, our study demonstrated that the aesthetic appeal of dynamic generative artworks could be predicted by integrating image features into the temporal domain.
已经开发出了几种自动化的美学评估模型，以帮助艺术家创作出具有高度审美吸引力的作品。然而，大多数模型都专注于静态视觉艺术，如照片和绘画，对动态艺术的评估则较少受到关注。动态视觉艺术，特别是基于计算机的艺术，包含多种形式的艺术表现，能够增强观众的审美体验。用于评估动态视觉艺术的模型可以为提高艺术技能和创造力提供有价值的反馈和指导，从而使观众受益。在本研究中，我们基于一种常用的方法，创作了 8 件具有动态艺术形式的生成艺术作品。我们建立了一个基于视觉特征的时变模型来预测美学效果。我们根据选定的图像特征对作品进行了量化，发现这些特征可以有效地捕捉生成过程中视觉形式变化的特征。为了探索时变特征对吸引力的影响，我们建立了一个面板回归模型，发现生成艺术作品的吸引力受到亮度分布的偏度、垂直对称性和平均色调值的显著影响。此外，我们的研究表明，通过将图像特征整合到时域中，可以预测动态生成艺术作品的吸引力。
