Vitruvion: A Generative Model of Parametric CAD Sketches

TL;DR

This paper introduces Vitruvion, a generative model for parametric CAD sketches that can synthesize, complete, and condition sketches, potentially accelerating mechanical design workflows by producing realistic and editable design primitives.

Contribution

The paper presents Vitruvion, a novel autoregressive generative model trained on real CAD sketches, capable of producing constraint-aware parametric sketches conditioned on various inputs.

Findings

Model accurately synthesizes realistic CAD sketches.

Samples are directly compatible with standard CAD software.

Conditional generation effectively completes and guides sketch design.

Abstract

Parametric computer-aided design (CAD) tools are the predominant way that engineers specify physical structures, from bicycle pedals to airplanes to printed circuit boards. The key characteristic of parametric CAD is that design intent is encoded not only via geometric primitives, but also by parameterized constraints between the elements. This relational specification can be viewed as the construction of a constraint program, allowing edits to coherently propagate to other parts of the design. Machine learning offers the intriguing possibility of accelerating the design process via generative modeling of these structures, enabling new tools such as autocompletion, constraint inference, and conditional synthesis. In this work, we present such an approach to generative modeling of parametric CAD sketches, which constitute the basic computational building blocks of modern mechanical design. Our model, trained on real-world designs from the SketchGraphs dataset, autoregressively synthesizes sketches as sequences of primitives, with initial coordinates, and constraints that reference back to the sampled primitives. As samples from the model match the constraint graph representation used in standard CAD software, they may be directly imported, solved, and edited according to downstream design tasks. In addition, we condition the model on various contexts, including partial sketches (primers) and images of hand-drawn sketches. Evaluation of the proposed approach demonstrates its ability to synthesize realistic CAD sketches and its potential to aid the mechanical design workflow.