用贝叶斯流网络实现参数化CAD的精准约束生成
Target-Guided Bayesian Flow Networks for Quantitatively Constrained CAD Generation
- 将离散命令与连续参数统一到可微分空间中建模
- 在多条件约束下生成高保真、符合要求的CAD序列
- 适合需要精确参数控制的工业设计生成场景
深度生成模型如扩散模型在图像和音频生成中表现优异，但针对参数化CAD序列等多模态数据的生成仍面临长程约束与参数敏感性挑战。本文提出目标引导贝叶斯流网络（TGBFN），首次在统一的连续可微参数空间中处理CAD序列的多模态特性（离散指令与连续参数）。TGBFN深入参数更新核心，引入引导贝叶斯流以控制CAD属性。为评估该方法，我们构建了新的定量约束CAD生成数据集。大量实验表明，TGBFN在单条件与多条件约束生成任务中均达到领先性能，能生成高保真且条件感知的CAD序列。代码已开源：https://github.com/scu-zwh/TGBFN。
原文摘要 · Abstract (English)
Deep generative models, such as diffusion models, have shown promising progress in image generation and audio generation via simplified continuity assumptions. However, the development of generative modeling techniques for generating multi-modal data, such as parametric CAD sequences, still lags behind due to the challenges in addressing long-range constraints and parameter sensitivity. In this work, we propose a novel framework for quantitatively constrained CAD generation, termed Target-Guided Bayesian Flow Network (TGBFN). For the first time, TGBFN handles the multi-modality of CAD sequences (i.e., discrete commands and continuous parameters) in a unified continuous and differentiable parameter space rather than in the discrete data space. In addition, TGBFN penetrates the parameter update kernel and introduces a guided Bayesian flow to control the CAD properties. To evaluate TGBFN, we construct a new dataset for quantitatively constrained CAD generation. Extensive comparisons across single-condition and multi-condition constrained generation tasks demonstrate that TGBFN achieves state-of-the-art performance in generating high-fidelity, condition-aware CAD sequences. The code is available at https://github.com/scu-zwh/TGBFN.
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