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Publication | International Conference on Machine Learning 2022

SkexGen

Autoregressive Generation of CAD Construction Sequences with Disentangled Codebooks

This Autodesk Research paper describes a new approach to generation of solid CAD models that enhances user control and enables efficient exploration of the design space.

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Abstract

SkexGen: Autoregressive Generation of CAD Construction Sequences with Disentangled Codebooks

Xiang Xu, Karl D.D. Willis, Joseph G. Lambourne, Chin-Yi Cheng, Pradeep Kumar Jayaraman, Yasutaka Furukawa

International Conference on Machine Learning 2022

We present SkexGen, a novel autoregressive generative model for computer-aided design (CAD) construction sequences containing sketch-and-extrude modeling operations. Our model utilizes distinct Transformer architectures to encode topological, geometric, and extrusion variations of construction sequences into disentangled codebooks. Autoregressive Transformer decoders generate CAD construction sequences sharing certain properties specified by the codebook vectors. Extensive experiments demonstrate that our disentangled codebook representation generates diverse and high-quality CAD models, enhances user control, and enables efficient exploration of the design space.

Associated Researchers

Karl Willis

Senior Manager, Research Science

Joseph George Lambourne

Senior Principal AI Research Scientist

Pradeep Kumar Jayaraman

Senior Manager, Research Science

Chin-Yi Cheng

Autodesk Research

Xiang Xu

Senior AI Research Scientist

Yasutaka Furukawa

Simon Fraser University

View all researchers

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