Text2CAD: Generating Sequential CAD Models from Beginner-to-Expert Level Text Prompts
<|reference_start|>Text2CAD: Generating Sequential CAD Models from Beginner-to-Expert Level Text Prompts: 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 $r_{1}$, $r_{2}$, 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. Our source code and annotations will be publicly available.<|reference_end|>
@article{khan2024text2cad:,
    title={Text2CAD: Generating Sequential CAD Models from Beginner-to-Expert Level
  Text Prompts},
    author={Mohammad Sadil Khan, Sankalp Sinha, Talha Uddin Sheikh, Didier
  Stricker, Sk Aziz Ali and Muhammad Zeshan Afzal},
    journal={arXiv preprint arXiv:2409.17106},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.17106},
    primaryClass={cs.CV cs.GR}
}