Wölfflin’s Affective Generative Analysis for Visual Art

We propose Wölfflin Affective Generative Analysis (WAGA) as an approach to understand and analyze the progress of machine-generated artworks in contrast to real art and their connection to our human artistic heritage, and how they extend the shape of art history.Specifically, we studied the machine-generated art after integrating creativity losses in the state-of-the-art generative models e.g., StyleGAN v1 and v2. We denote these models as Style Creative Adversarial Networks v1 and v2; in short, StyleCAN v1 and v2. We contrasted the learned representation between real and generated artworks through correlation analysis between constructed emotion (collected through Amazon MTurk) and Heinrich Wölfflin (1846-1945)’s principles of art history. Analogous to the recent ArtEmis dataset, we collected constructed emotions and explanations on generated art instead of real art to study the contrast. To enable Wölfflin Affective Generative Analysis, we collected 45,000 annotations (1800 paintings ×5 principles ×5 participants) for each of the five Wölfflin principles on 1800 artworks; 1000 real and 800 generated. Our analysis shows a correlation exists between the Wölfflin principles and the emotions. The collected dataset, analysis, and code is made publicly available at https://vision-cair.github.io/WAGA.