A Computer-Aided Design (CAD) model encodes an object in two coupled forms: a parametric construction sequence and its resulting visible geometric shape. During iterative design, adjustments to the geometric shape inevitably require synchronized edits to the underlying parametric sequence, called geometry-driven parametric CAD editing. The task calls for 1) preserving the original sequence's structure, 2) ensuring each edit's semantic validity, and 3) maintaining high shape fidelity to the target shape, all under scarce editing data triplets. We present CADMorph, an iterative plan-generate-verify framework that orchestrates pretrained domain-specific foundation models during inference: a parameter-to-shape (P2S) latent diffusion model and a masked-parameter-prediction (MPP) model. In the planning stage, cross-attention maps from the P2S model pinpoint the segments that need modification and offer editing masks. The MPP model then infills these masks with semantically valid edits in the generation stage. During verification, the P2S model embeds each candidate sequence in shape-latent space, measures its distance to the target shape, and selects the closest one. The three stages leverage the inherent geometric consciousness and design knowledge in pretrained priors, and thus tackle structure preservation, semantic validity, and shape fidelity respectively. Besides, both P2S and MPP models are trained without triplet data, bypassing the data-scarcity bottleneck. CADMorph surpasses GPT-4o and specialized CAD baselines, and supports downstream applications such as iterative editing and reverse-engineering enhancement.

翻译：计算机辅助设计（CAD）模型以两种耦合形式编码对象：参数化构建序列及其生成的可见几何形状。在迭代设计过程中，对几何形状的调整不可避免地需要同步编辑底层的参数化序列，这称为几何驱动的参数化CAD编辑。该任务要求在编辑数据三元组稀缺的情况下，实现：1）保持原始序列的结构，2）确保每次编辑的语义有效性，以及3）维持与目标形状的高形状保真度。我们提出了CADMorph，一种迭代的规划-生成-验证框架，在推理过程中协调预训练的领域特定基础模型：参数到形状（P2S）潜在扩散模型和掩码参数预测（MPP）模型。在规划阶段，来自P2S模型的交叉注意力图精确定位需要修改的片段并提供编辑掩码。随后，MPP模型在生成阶段用语义有效的编辑内容填充这些掩码。在验证阶段，P2S模型将每个候选序列嵌入形状潜在空间，测量其与目标形状的距离，并选择最接近的一个。这三个阶段利用了预训练先验中固有的几何意识与设计知识，从而分别处理结构保持、语义有效性和形状保真度。此外，P2S和MPP模型均无需三元组数据训练，绕过了数据稀缺的瓶颈。CADMorph超越了GPT-4o和专用CAD基线模型，并支持迭代编辑和逆向工程增强等下游应用。