3D-PreMise: Can Large Language Models Generate 3D Shapes with Sharp Features and Parametric Control?

• URL: http://arxiv.org/abs/2401.06437v1
• Date: Fri, 12 Jan 2024 08:07:52 GMT
• Title: 3D-PreMise: Can Large Language Models Generate 3D Shapes with Sharp Features and Parametric Control?
• Authors: Zeqing Yuan, Haoxuan Lan, Qiang Zou, Junbo Zhao
• Abstract summary: We introduce a framework that employs Large Language Models to generate text-driven 3D shapes. We present 3D-PreMise, a dataset specifically tailored for 3D parametric modeling of industrial shapes.
• Score: 8.893200442359518
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recent advancements in implicit 3D representations and generative models have markedly propelled the field of 3D object generation forward. However, it remains a significant challenge to accurately model geometries with defined sharp features under parametric controls, which is crucial in fields like industrial design and manufacturing. To bridge this gap, we introduce a framework that employs Large Language Models (LLMs) to generate text-driven 3D shapes, manipulating 3D software via program synthesis. We present 3D-PreMise, a dataset specifically tailored for 3D parametric modeling of industrial shapes, designed to explore state-of-the-art LLMs within our proposed pipeline. Our work reveals effective generation strategies and delves into the self-correction capabilities of LLMs using a visual interface. Our work highlights both the potential and limitations of LLMs in 3D parametric modeling for industrial applications.

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