title: CAD-Editor: A Locate-then-Infill Framework with Automated Training Data
    Synthesis for Text-Based CAD Editing
summary: 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.
paper_authors._id: 67ac206214d5fe7767e7ec4e
paper_authors.name: Yu Yuan
paper_authors._id: 67ac206214d5fe7767e7ec4f
paper_authors.name: Shizhao Sun
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-02-12T09:16:14.580Z
paper_authors._id: 63eb00a191a1b8ec4fbba2a9
paper_authors.avatarUrl: /avatars/0cc7cf9b6d05337603f700e0d592edf5.svg
paper_authors.fullname: ShizhaoSun
paper_authors.type: user
paper_authors.user: ShizhaoSun
paper_authors._id: 67ac206214d5fe7767e7ec50
paper_authors.name: Qi Liu
paper_authors._id: 67ac206214d5fe7767e7ec51
paper_authors.name: Jiang Bian
paper_title: CAD-Editor: A Locate-then-Infill Framework with Automated Training Data
    Synthesis for Text-Based CAD Editing
paper_summary: 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.
paper_ai_summary: 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.
paper_ai_keywords: 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