Text2CAD

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 ∼170K models and ∼660K text annotations, from abstract CAD descriptions (e.g., \textit{generate two concentric cylinders}) to detailed specifications (e.g., \textit{draw two circles with center} (x,y) \textit{and radius} r₁, r₂, \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~\href{https://sadilkhan.github.io/text2cad-project/}{https://sadilkhan.github.io/text2cad-project/}.