Text2VP: Generative AI for Visual Programming and Parametric Modeling

TL;DR

Text2VP introduces a GPT-based generative AI system that automates visual programming workflows for parametric modeling, enabling designers to generate and modify models through natural language instructions.

Contribution

The paper presents a novel GPT-4.1-based AI tool that automates graph-based visual programming for parametric models, supporting interactive adjustments and aiming to simplify complex design tasks.

Findings

Successfully generates functional parametric models from text prompts

Higher complexity models increase error rates

Demonstrates potential for AI-assisted visual programming

Abstract

The integration of generative artificial intelligence (AI) into architectural design has advanced significantly, enabling the generation of text, images, and 3D models. However, prior AI applications lack support for text-to-parametric models, essential for generating and optimizing diverse parametric design options. This study introduces Text-to-Visual Programming (Text2VP) GPT, a novel generative AI derived from GPT-4.1, designed to automate graph-based visual programming workflows, parameters, and their interconnections. Text2VP leverages detailed documentation, specific instructions, and example-driven few-shot learning to reflect user intentions accurately and facilitate interactive parameter adjustments. Testing demonstrates Text2VP's capability in generating functional parametric models, although higher complexity models present increased error rates. This research highlights generative AI's potential in visual programming and parametric modeling, laying groundwork for future improvements to manage complex modeling tasks. Ultimately, Text2VP aims to enable designers to easily create and modify parametric models without extensive training in specialized platforms like Grasshopper.