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Poster
in
Workshop: NeurIPS 2023 Workshop on Machine Learning for Creativity and Design

CAD-LLM: Large Language Model for CAD Generation

Sifan Wu · Amir Khasahmadi · Mor Katz · Pradeep Kumar Jayaraman · Yewen Pu · Karl Willis · Bang Liu


Abstract:

Parametric Computer-Aided Design (CAD) is the dominant paradigm for modernmechanical design. Training generative models to reason and generate parametricCAD can dramatically speed up design workflows. Pre-trained foundation modelshave shown great success in natural language processing and computer vision. The cross-domain knowledge embedded in these models holds significant potential for understanding geometry and performing complex reasoning about design. In this work, we develop generative models for CAD by leveraging pre-trained language models and apply them to manipulate engineering sketches. Our results demonstrate that models pre-trained on natural language can be finetuned on engineering sketches and achieve remarkable performance in various CAD generation scenarios.

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