We present SketchDNN, a generative model for synthesizing CAD sketches that jointly models both continuous parameters and discrete class labels through a unified continuous-discrete diffusion process. Our core innovation is Gaussian-Softmax diffusion, where logits perturbed with Gaussian noise are projected onto the probability simplex via a softmax transformation, facilitating blended class labels for discrete variables. This formulation addresses 2 key challenges, namely, the heterogeneity of primitive parameterizations and the permutation invariance of primitives in CAD sketches. Our approach significantly improves generation quality, reducing Fr\'echet Inception Distance (FID) from 16.04 to 7.80 and negative log-likelihood (NLL) from 84.8 to 81.33, establishing a new state-of-the-art in CAD sketch generation on the SketchGraphs dataset.

翻译：本文提出SketchDNN，一种用于合成CAD草图的生成模型，其通过统一的连续-离散扩散过程联合建模连续参数与离散类别标签。我们的核心创新是高斯-softmax扩散，其中经高斯噪声扰动的logits通过softmax变换投影到概率单纯形上，从而为离散变量实现混合类别标签。该公式解决了两个关键挑战：基元参数化的异构性以及CAD草图中基元的置换不变性。我们的方法显著提升了生成质量，将Fréchet Inception Distance（FID）从16.04降至7.80，负对数似然（NLL）从84.8降至81.33，在SketchGraphs数据集上实现了CAD草图生成的新最优性能。