标题： SketchDNN：用于CAD草图生成的联合连续-离散扩散模型 显示英文标题

标题： SketchDNN: Joint Continuous-Discrete Diffusion for CAD Sketch Generation

摘要： 我们提出SketchDNN，这是一种生成模型，用于合成CAD草图，通过统一的连续-离散扩散过程联合建模连续参数和离散类别标签。 我们的核心创新是高斯-softmax扩散，其中通过softmax变换将加入高斯噪声的logits投影到概率单纯形上，促进离散变量的混合类别标签。 这种公式解决了两个关键挑战，即基本参数化的异质性和CAD草图中基本元素的排列不变性。 我们的方法显著提高了生成质量，将Fréchet Inception距离（FID）从16.04降低到7.80，负对数似然（NLL）从84.8降低到81.33，在SketchGraphs数据集上建立了CAD草图生成的新最先进水平。 显示英文摘要

摘要： 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.