AI Co-Artist: A LLM-Powered Framework for Interactive GLSL Shader Animation Evolution

• URL: http://arxiv.org/abs/2512.08951v1
• Date: Thu, 27 Nov 2025 18:55:32 GMT
• Title: AI Co-Artist: A LLM-Powered Framework for Interactive GLSL Shader Animation Evolution
• Authors: Kamer Ali Yuksel, Hassan Sawaf,
• Abstract summary: We present AI Co-Artist, a novel interactive system that harnesses the capabilities of large language models (LLMs), specifically GPT-4.<n>Our system empowers users to evolve shader art using intuitive interactions, without needing to write or understand code.
• Score: 2.7010154811483162
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Creative coding and real-time shader programming are at the forefront of interactive digital art, enabling artists, designers, and enthusiasts to produce mesmerizing, complex visual effects that respond to real-time stimuli such as sound or user interaction. However, despite the rich potential of tools like GLSL, the steep learning curve and requirement for programming fluency pose substantial barriers for newcomers and even experienced artists who may not have a technical background. In this paper, we present AI Co-Artist, a novel interactive system that harnesses the capabilities of large language models (LLMs), specifically GPT-4, to support the iterative evolution and refinement of GLSL shaders through a user-friendly, visually-driven interface. Drawing inspiration from the user-guided evolutionary principles pioneered by the Picbreeder platform, our system empowers users to evolve shader art using intuitive interactions, without needing to write or understand code. AI Co-Artist serves as both a creative companion and a technical assistant, allowing users to explore a vast generative design space of real-time visual art. Through comprehensive evaluations, including structured user studies and qualitative feedback, we demonstrate that AI Co-Artist significantly reduces the technical threshold for shader creation, enhances creative outcomes, and supports a wide range of users in producing professional-quality visual effects. Furthermore, we argue that this paradigm is broadly generalizable. By leveraging the dual strengths of LLMs-semantic understanding and program synthesis, our method can be applied to diverse creative domains, including website layout generation, architectural visualizations, product prototyping, and infographics.

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