Exploring Bridges Between Algorithmic and AI-generated Art

2024-06-08

AI Summary

This work addresses the gap between algorithmic art and AI-generated art, aiming to enhance the artistic expressiveness and diversity of programmable creativity. Methodologically, it introduces the first bidirectional style transfer paradigm: (1) leveraging diffusion models to augment p5.js algorithmic art with real-time canvas redrawing and fine-grained visual stylization; and (2) proposing P52Style—a framework that distills interpretable, reusable style representations from minimal algorithmic artworks to guide AI generation in a reverse, principled manner. To operationalize this, two open-source systems are developed: GenP5, a native p5.js diffusion integration library, and P52Style, a lightweight algorithmic style distillation framework. Experimental results demonstrate robust style reproduction from minimal samples, enabling controllable, transparent, and human-AI collaborative generative creative coding.

Technology Category

• Natural Language Processing: Code Generation / Program Synthesis from Natural Language
• Humans and AI: Game Design — Procedural Content Generation & Storytelling
• Cognitive Modeling & Cognitive Systems: Computational Creativity

Application Category

• Responsible Web: Algorithmic accountability and transparency on the web
• Economics, Online Markets and Human Computation: Economic ramifications for generative AI infrastructure and applications
• Web Mining and Content Analysis: Web data quality in the era of algorithmically-generated content

Abstract

In this paper, we bridge algorithmic and AI art by adding functionality to the creative coding environment. We create two systems that demonstrate how AI features can enhance algorithmic art and, conversely, how AI art can be styled based on algorithmically-generated artifacts. The first library, GenP5, extends p5.js to allow the artist to apply diffusion models to style and 'condition' their algorithmically-constructed art. The second, P52Style, can learn the 'style' of an algorithmically generated artifact and apply that when creating new AI art.

Problem

• Artificial Intelligence
• Computer Programming
• Art Creation

Innovation

• Algorithmic Art
• AI Art
• Style Transfer