CAD-Tokenizer: Towards Text-based CAD Prototyping via Modality-Specific
  Tokenization
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.
68da804fd2bf1f4b15ec7122
Ruiyu Wang
claimed_verified
2025-09-29T13:05:05.871Z
67a30a243d5a32b36c7d7d0b
/avatars/4aa43f0d79797be1063f246d63638a85.svg
Ruiyu Wang
user
rywang37
68da804fd2bf1f4b15ec7123
Shizhao Sun
68da804fd2bf1f4b15ec7124
Weijian Ma
68da804fd2bf1f4b15ec7125
Jiang Bian
CAD-Tokenizer: Towards Text-based CAD Prototyping via Modality-Specific
  Tokenization
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.
CAD-Tokenizer, a multimodal tokenization framework using VQ-VAE, enhances text-guided CAD prototyping by improving instruction following and generation quality.
VQ-VAE multimodal tokenization primitive-level pooling constrained decoding text-guided CAD prototyping