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SkexGen: Autoregressive Generation of CAD Construction Sequences with Disentangled Codebooks

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arxiv 2207.04632 v1 pith:2JK6AUOO submitted 2022-07-11 cs.CV cs.LG

Xiang Xu , Karl D.D. Willis , Joseph G. Lambourne , Chin-Yi Cheng , Pradeep Kumar Jayaraman , Yasutaka Furukawa This is my paper
classification cs.CVcs.LG
keywords constructionsequencesautoregressivedisentangledskexgencodebookcodebooksdesign
verification ladder T0 review T1 audit T2 compute T3 formal
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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. The code is available at https://samxuxiang.github.io/skexgen.

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Cited by 6 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. OpenAlex reports about 12 citations worldwide. Full citation record

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    CAD-IR scaffolds ambiguous text into executable CATIA procedures and, with expert-distilled skills, produces editable B-Rep variants of complex automotive parts.

  4. SketchDNN: Joint Continuous-Discrete Diffusion for CAD Sketch Generation

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    A Gaussian-Softmax diffusion model jointly generates continuous parameters and discrete class labels for CAD sketches, reporting better FID and NLL than autoregressive and categorical diffusion baselines on SketchGraphs.

  5. GraphBrep: Learning B-Rep in Graph Structure for Efficient CAD Generation

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    cs.LG 2026-07 conditional novelty 5.0 of 10

    A wrong design-intent header degrades CAD completion below the no-header baseline, and a derangement-trained control shows the harm comes from the learned header-program mapping.

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