An Abstract Painting Generation Method Based on Deep Generative Model

Computer technology provides new conditions and possibilities for art creation and research, and also expands the forms of artistic expression. Computer-created art has thus become one of the important forms of art. In this paper, we proposed a novel method of generating abstract paintings. We used the public painting dataset WikiArt and designed a K-Means algorithm that automatically finds the optimal K value to perform color segmentation on these images, and divide the picture into different color blocks. We proposed the concept of the collection of color block (CoCB), which records all color block information of the segmented image and serves as an intermediate vector for the generation of abstract painting. We extracted the CoCB as an empirical sample and used a learning model based on deep learning to automatically generate brand-new CoCBs. We then converted the CoCBs into an abstract painting, so that the generated abstract painting also followed certain aesthetic rules. Experiments showed that the resulting abstract painting have great visual impact, and some of them have been installed as decorations in public and private spaces, as well as art institutions. Also, some artists and designers have used the results in their work.