Exploring Bridges Between Creative Coding and Visual Generative AI (2406.05508v1)

Abstract: How to bridge generative procedural art and visual generative AI for visual content creation is an under-explored topic. On the one hand, there are many cases where creative programmers can make use of generative AI, including stylizing canvas content and creating new content based on the existing styles of certain procedural art (style learning). On the other hand, existing approaches don't support creative programmers to flexibly leverage visual generative AI methods within the creative coding environment. In this work, we explore how to bridge generative procedural art creation and visual generative AI (specifically diffusion models) by programming functionalities integrated into the creative environment. Specifically, we want to explore methodologies to condition/stylize art content and perform style learning upon procedural art via accessible interactions for artists and programmers. We proposed two methods: GenP5, a novel p5.js library enabling generative procedural art creation with flexibly stylizing canvas content and conveniently condition art creation with pre-determined patterns; and P52Style, an extended library built upon p5.gui allowing flexible adjustment of art content and leverage of visual generative AI for style learning tasks.