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.

翻译：深度生成模型（如扩散模型）通过简化的连续性假设，在图像生成与音频生成领域展现出显著进展。然而，针对多模态数据（如参数化CAD序列）的生成建模技术发展仍相对滞后，这主要源于长程约束处理与参数敏感性带来的挑战。本研究提出一种新颖的定量约束CAD生成框架，称为目标引导贝叶斯流网络（TGBFN）。该框架首次在统一的连续可微参数空间（而非离散数据空间）中处理CAD序列的多模态特性（即离散指令与连续参数）。此外，TGBFN通过渗透参数更新核并引入引导贝叶斯流，实现对CAD属性的精准调控。为评估TGBFN性能，我们构建了面向定量约束CAD生成的新数据集。在单条件与多条件约束生成任务上的广泛对比实验表明，TGBFN在生成高保真度、条件感知的CAD序列方面达到了最先进的性能。代码已开源：https://github.com/scu-zwh/TGBFN。