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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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
Chin-Yi Cheng
Autodesk Research
Yasutaka Furukawa
Simon Fraser University
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