UniSketch: A Unified Framework for Parametric Sketch Generation and Constraint Prediction

Jing Lin, Fazhi He, Rubin Fan

Abstract: In modern Computer-Aided Design (CAD), parametric sketches play a crucial role by capturing both the geometric structure and design intent through constraints. However, existing deep learning–based sketch methods remain restricted to simple geometric primitives and limited constraint types, hindering their application to complex realworld engineering tasks. To address this gap, we introduce the UniSketch dataset, comprising 3,836,290 sketches. It offers a comprehensive and diverse collection of 7 types of geometric primitives and 23 types of 2D constraints, all represented as unified vector sequences suitable for deep learning applications. Leveraging the UniSketch dataset, we propose a unified multi-task Transformer framework as a true foundation model for parametric sketch modeling, supporting diverse core tasks like image-to-sketch generation, constraint prediction, and unconditional sketch synthesis. Furthermore, the generated sketches can be efficiently converted to CAD-compatible formats, enabling seamless integration with industrial CAD system for re-editing and reusing. The experimental results show that UniSketch outperforms existing methods in multiple tasks, demonstrating its versatility and practical value in industrial CAD applications.