CAD-Editor: A Locate-then-Infill Framework with Automated Training Data
  Synthesis for Text-Based CAD Editing
Authors: name: Yu Yuan, name: Shizhao Sun, name: Qi Liu, name: Jiang Bian

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.