Generative Art Using Neural Visual Grammars and Dual Encoders
This work addresses the challenge of understanding and automating open-ended artistic creation for artists and researchers, though it appears incremental as it builds on existing neural and dual encoder methods.
The paper tackles the problem of automating artistic processes by developing a novel algorithm that generates images from text inputs using a hierarchical neural Lindenmeyer system and evaluates them with a dual encoder trained on billions of image-text pairs, resulting in a system that interprets text creatively to produce generative art.
Whilst there are perhaps only a few scientific methods, there seem to be almost as many artistic methods as there are artists. Artistic processes appear to inhabit the highest order of open-endedness. To begin to understand some of the processes of art making it is helpful to try to automate them even partially. In this paper, a novel algorithm for producing generative art is described which allows a user to input a text string, and which in a creative response to this string, outputs an image which interprets that string. It does so by evolving images using a hierarchical neural Lindenmeyer system, and evaluating these images along the way using an image text dual encoder trained on billions of images and their associated text from the internet. In doing so we have access to and control over an instance of an artistic process, allowing analysis of which aspects of the artistic process become the task of the algorithm, and which elements remain the responsibility of the artist.