# Text2VP: Generative AI for Visual Programming and Parametric Modeling

arXiv: https://arxiv.org/abs/2407.07732 · HTML: https://mlsanity.com/paper/2407.07732/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 11 references that are in the map.
- First posted on arXiv 9 Jun 2024.

## Builds on (most cited first, 3 of 3)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [TextMesh: Generation of Realistic 3D Meshes From Text Prompts](https://mlsanity.com/paper/2304.12439/) (2023)
- [Sketch2Mesh: Reconstructing and Editing 3D Shapes from Sketches](https://mlsanity.com/paper/2104.00482/) (2021)

## Abstract (arXiv)

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.