CAD-Editor: A Locate-then-Infill Framework with Automated Training Data
  Synthesis for Text-Based CAD Editing
Computer Aided Design (CAD) is indispensable across various industries.
Text-based CAD editing, which automates the modification of CAD models
based on textual instructions, holds great potential but remains underexplored.
Existing methods primarily focus on design variation generation or text-based
CAD generation, either lacking support for text-based control or neglecting
existing CAD models as constraints. We introduce CAD-Editor, the first
framework for text-based CAD editing. To address the challenge of demanding
triplet data with accurate correspondence for training, we propose an automated
data synthesis pipeline. This pipeline utilizes design variation models to
generate pairs of original and edited CAD models and employs Large
Vision-Language Models (LVLMs) to summarize their differences into editing
instructions. To tackle the composite nature of text-based CAD editing, we
propose a locate-then-infill framework that decomposes the task into two
focused sub-tasks: locating regions requiring modification and infilling these
regions with appropriate edits. Large Language Models (LLMs) serve as the
backbone for both sub-tasks, leveraging their capabilities in natural language
understanding and CAD knowledge. Experiments show that CAD-Editor achieves
superior performance both quantitatively and qualitatively.
67ac206214d5fe7767e7ec4e
Yu Yuan
67ac206214d5fe7767e7ec4f
Shizhao Sun
claimed_verified
2025-02-12T09:16:14.580Z
63eb00a191a1b8ec4fbba2a9
/avatars/0cc7cf9b6d05337603f700e0d592edf5.svg
ShizhaoSun
user
ShizhaoSun
67ac206214d5fe7767e7ec50
Qi Liu
67ac206214d5fe7767e7ec51
Jiang Bian
claimed_verified
2025-06-19T09:11:10.221Z
683843c8a9bdf98f92657042
/avatars/4983876f615fa830cfc8f2cb5a00fa08.svg
Jiang Bian
user
jiangbian22
CAD-Editor: A Locate-then-Infill Framework with Automated Training Data
  Synthesis for Text-Based CAD Editing
Computer Aided Design (CAD) is indispensable across various industries.
Text-based CAD editing, which automates the modification of CAD models
based on textual instructions, holds great potential but remains underexplored.
Existing methods primarily focus on design variation generation or text-based
CAD generation, either lacking support for text-based control or neglecting
existing CAD models as constraints. We introduce CAD-Editor, the first
framework for text-based CAD editing. To address the challenge of demanding
triplet data with accurate correspondence for training, we propose an automated
data synthesis pipeline. This pipeline utilizes design variation models to
generate pairs of original and edited CAD models and employs Large
Vision-Language Models (LVLMs) to summarize their differences into editing
instructions. To tackle the composite nature of text-based CAD editing, we
propose a locate-then-infill framework that decomposes the task into two
focused sub-tasks: locating regions requiring modification and infilling these
regions with appropriate edits. Large Language Models (LLMs) serve as the
backbone for both sub-tasks, leveraging their capabilities in natural language
understanding and CAD knowledge. Experiments show that CAD-Editor achieves
superior performance both quantitatively and qualitatively.
CAD-Editor is a framework for text-based CAD editing that uses a locate-then-infill strategy and combines design variation models and Large Vision-Language Models for high-quality, text-guided modifications of existing CAD models.
Text-based CAD editing CAD-Editor automated data synthesis pipeline design variation models Large Vision-Language Models (LVLMs) editing instructions locate-then-infill framework Large Language Models (LLMs) natural language understanding CAD knowledge