CAD-Tokenizer: Towards Text-based CAD Prototyping via Modality-Specific
  Tokenization
Authors: name: Ruiyu Wang, name: Shizhao Sun, name: Weijian Ma, name: Jiang Bian
Organization: Microsoft

Computer-Aided Design (CAD) is a foundational component of industrial
prototyping, where models are defined not by raw coordinates but by
construction sequences such as sketches and extrusions. This sequential
structure enables both efficient prototype initialization and subsequent
editing. Text-guided CAD prototyping, which unifies Text-to-CAD generation and
CAD editing, has the potential to streamline the entire design pipeline.
However, prior work has not explored this setting, largely because standard
large language model (LLM) tokenizers decompose CAD sequences into
natural-language word pieces, failing to capture primitive-level CAD semantics
and hindering attention modules from modeling geometric structure. We
conjecture that a multimodal tokenization strategy, aligned with CAD's
primitive and structural nature, can provide more effective representations. To
this end, we propose CAD-Tokenizer, a framework that represents CAD data with
modality-specific tokens using a sequence-based VQ-VAE with primitive-level
pooling and constrained decoding. This design produces compact, primitive-aware
representations that align with CAD's structural nature. Applied to unified
text-guided CAD prototyping, CAD-Tokenizer significantly improves instruction
following and generation quality, achieving better quantitative and qualitative
performance over both general-purpose LLMs and task-specific baselines.