Salvato in:
| Autori principali: | , , , |
|---|---|
| Natura: | Preprint |
| Pubblicazione: |
2024
|
| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2401.06437 |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866914641719853056 |
|---|---|
| author | Yuan, Zeqing Lan, Haoxuan Zou, Qiang Zhao, Junbo |
| author_facet | Yuan, Zeqing Lan, Haoxuan Zou, Qiang Zhao, Junbo |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_06437 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | 3D-PreMise: Can Large Language Models Generate 3D Shapes with Sharp Features and Parametric Control? Yuan, Zeqing Lan, Haoxuan Zou, Qiang Zhao, Junbo Graphics Artificial Intelligence Computation and Language 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. |
| title | 3D-PreMise: Can Large Language Models Generate 3D Shapes with Sharp Features and Parametric Control? |
| topic | Graphics Artificial Intelligence Computation and Language |
| url | https://arxiv.org/abs/2401.06437 |