@inproceedings{
khan2024textcad,
title={Text2{CAD}: Generating Sequential {CAD} Designs from Beginner-to-Expert Level Text Prompts},
author={Mohammad Sadil Khan and Sankalp Sinha and Sheikh Talha Uddin and Didier Stricker and Sk Aziz Ali and Muhammad Zeshan Afzal},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=5k9XeHIK3L}
}
Prototyping complex computer-aided design (CAD) models in modern softwares can be very time-consuming. This is due to the lack of intelligent systems that can quickly generate simpler intermediate parts. We propose Text2CAD, the first AI framework for generating text-to-parametric CAD models using designer-friendly instructions for all skill levels. Furthermore, we introduce a data annotation pipeline for generating text prompts based on natural language instructions for the DeepCAD dataset using Mistral and LLaVA-NeXT. The dataset contains $\sim170$K models and $\sim660$K text annotations, from abstract CAD descriptions (e.g., _generate two concentric cylinders_) to detailed specifications (e.g., _draw two circles with center_ $(x,y)$ and _radius_ $r_{1}$, $r_{2}$, \textit{and extrude along the normal by} $d$...). Within the Text2CAD framework, we propose an end-to-end transformer-based auto-regressive network to generate parametric CAD models from input texts. We evaluate the performance of our model through a mixture of metrics, including visual quality, parametric precision, and geometrical accuracy. Our proposed framework shows great potential in AI-aided design applications. Project page is available at https://sadilkhan.github.io/text2cad-project/.
Text2CAD: Generating Sequential CAD Designs from Beginner-to-Expert Level Text Prompts
Mohammad Sadil Khan Sankalp Sinha Sheikh Talha Uddin Didier Stricker Sk Aziz Ali Muhammad Zeshan Afzal