Zero-shot CAD Program Re-Parameterization for Interactive Manipulation
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Abstract (arXiv)
Parametric CAD models encode entire families of shapes that should, in principle, be easy for designers to explore. However, in practice, parametric CAD models can be difficult to manipulate due to implicit semantic constraints among parameter values. Finding and enforcing these semantic constraints solely from geometry or programmatic shape representations is not possible because these constraints ultimately reflect design intent. They are informed by the designer's experience and semantics in the real world. To address this challenge, we introduce a zero-shot pipeline that leverages pre-trained large language and image model to infer meaningful space of variations for a shape. We then re-parameterize a new constrained parametric CAD program that captures these variations, enabling effortless exploration of the design space along meaningful design axes.
Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). First posted on arXiv 5 Jun 2023.
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Citing papers per year: 2023: 1; 2024: 2; 2025: 0; 2026: 1.
4 papers.
- 2023Hierarchical Neural Coding for Controllable CAD Model Generationcited by 84
- 2024Generating CAD Code with Vision-Language Models for 3D Designscited by 61
- 2024DI-PCG: Diffusion-based Efficient Inverse Procedural Content Generation for High-quality 3D Asset Creationcited by 7
- 2026TOOLCAD: Exploring Tool-Using Large Language Models in Text-to-CAD Generation with Reinforcement Learningcited by 6
- 2026TOOLCAD: Exploring Tool-Using Large Language Models in Text-to-CAD Generation with Reinforcement Learning9 Apr 2026
- 2024DI-PCG: Diffusion-based Efficient Inverse Procedural Content Generation for High-quality 3D Asset Creation19 Dec 2024
- 2024Generating CAD Code with Vision-Language Models for 3D Designs7 Oct 2024
- 2023Hierarchical Neural Coding for Controllable CAD Model Generation30 Jun 2023