NURBGen: High-Fidelity Text-to-CAD Generation through LLM-Driven NURBS
  Modeling
Generating editable 3D CAD models from natural language remains challenging,
as existing text-to-CAD systems either produce meshes or rely on scarce
design-history data. We present NURBGen, the first framework to generate
high-fidelity 3D CAD models directly from text using Non-Uniform Rational
B-Splines (NURBS). To achieve this, we fine-tune a large language model (LLM)
to translate free-form texts into JSON representations containing NURBS surface
parameters (i.e, control points, knot vectors, degrees, and rational
weights) which can be directly converted into BRep format using Python. We
further propose a hybrid representation that combines untrimmed NURBS with
analytic primitives to handle trimmed surfaces and degenerate regions more
robustly, while reducing token complexity. Additionally, we introduce partABC,
a curated subset of the ABC dataset consisting of individual CAD components,
annotated with detailed captions using an automated annotation pipeline.
NURBGen demonstrates strong performance on diverse prompts, surpassing prior
methods in geometric fidelity and dimensional accuracy, as confirmed by expert
evaluations. Code and dataset will be released publicly.
69131472a644ba07c499c8a9
Muhammad Usama
claimed_verified
2025-11-11T19:41:35.777Z
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/avatars/0ca98a2b24f598ce02bf23ddbc6a7ecf.svg
Muhammad Usama
MUsama100
user
MUsama100
69131472a644ba07c499c8aa
Mohammad Sadil Khan
claimed_verified
2025-11-11T19:41:33.445Z
65e07fb40d364b871d406648
https://cdn-avatars.huggingface.co/v1/production/uploads/65e07fb40d364b871d406648/ye6D56YpOyL8bHoTVmFcq.png
Mohammad Sadil Khan
SadilKhan
user
SadilKhan
69131472a644ba07c499c8ab
Didier Stricker
69131472a644ba07c499c8ac
Muhammad Zeshan Afzal
NURBGen: High-Fidelity Text-to-CAD Generation through LLM-Driven NURBS
  Modeling
Generating editable 3D CAD models from natural language remains challenging,
as existing text-to-CAD systems either produce meshes or rely on scarce
design-history data. We present NURBGen, the first framework to generate
high-fidelity 3D CAD models directly from text using Non-Uniform Rational
B-Splines (NURBS). To achieve this, we fine-tune a large language model (LLM)
to translate free-form texts into JSON representations containing NURBS surface
parameters (i.e, control points, knot vectors, degrees, and rational
weights) which can be directly converted into BRep format using Python. We
further propose a hybrid representation that combines untrimmed NURBS with
analytic primitives to handle trimmed surfaces and degenerate regions more
robustly, while reducing token complexity. Additionally, we introduce partABC,
a curated subset of the ABC dataset consisting of individual CAD components,
annotated with detailed captions using an automated annotation pipeline.
NURBGen demonstrates strong performance on diverse prompts, surpassing prior
methods in geometric fidelity and dimensional accuracy, as confirmed by expert
evaluations. Code and dataset will be released publicly.
NURBGen generates high-fidelity 3D CAD models from text using Non-Uniform Rational B-Splines, outperforming existing methods in geometric fidelity and dimensional accuracy.
Non-Uniform Rational B-Splines NURBS large language model LLM JSON representations BRep format hybrid representation analytic primitives trimmed surfaces degenerate regions partABC ABC dataset automated annotation pipeline