arXiv:2605.28579v1 Announce Type: new Abstract: Large language models (LLMs) have recently advanced text-driven 3D generation, yet Text-to-CAD remains far from supporting industrial product design. Existing benchmarks focus primarily on generating single-part CAD models and evaluate them using geometric similarity metrics that fail to capture functionality, manufacturability, and assemblability. To address this gap, we introduce MUSE, a Text-to-CAD benchmark focused on complex, editable boundary representation (B-Rep) assemblies. MUSE pairs practical design instances with structured Design Spe
The rapid advancements in LLMs and text-driven 3D generation necessitate more robust benchmarks to bridge the gap between academic progress and industrial application in CAD.
This benchmark is crucial for maturing Text-to-CAD technology beyond simple geometric outputs, enabling practical industrial product design with considerations for functionality, manufacturability, and assembly.
The introduction of MUSE shifts the focus of Text-to-CAD evaluation from basic geometric similarity to complex, multi-part assemblies with practical design criteria, making the technology more useful for real-world engineering.
- · Industrial design software companies
- · Manufacturing sector
- · AI-driven design tool developers
- · Engineers and product designers
- · Companies reliant solely on traditional CAD methods
- · Developers of Text-to-3D models with poor industrial utility
Improved Text-to-CAD systems will accelerate product development cycles and reduce design costs in various industries.
The ability to rapidly generate manufacturable and assemblable designs from text could democratize complex product design and foster distributed manufacturing.
This could lead to a new paradigm of 'prompt-to-product' workflows, where AI agents design, simulate, and optimize complex physical objects with minimal human intervention.
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Read at arXiv cs.AI