Generative Art Using Neural Visual Grammars and Dual Encoders
MAY. 11, 2021
GANs got a competitor???
A new algorithm that generates paintings using a neural visual grammar system evaluated using a text conditioned dual encoder. This allows for the creation of images that look very different from those made by GANs.
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 under- stand 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 gen- erative 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 inter- prets 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.
Link to the paper: https://arxiv.org/pdf/2105.00162.pdf