Generative Engineering Design approaches driven by Deep Generative Models (DGM) have been proposed to facilitate industrial engineering processes. In such processes, designs often come in the form of images, such as blueprints, engineering drawings, and CAD models depending on the level of detail. DGMs have been successfully employed for synthesis of natural images, e.g., displaying animals, human faces and landscapes. However, industrial design images are fundamentally different from natural scenes in that they contain rich structural patterns and long-range dependencies, which are challenging for convolution-based DGMs to generate. Moreover, DGM-driven generation process is typically triggered based on random noisy inputs, which outputs unpredictable samples and thus cannot perform an efficient industrial design exploration. We tackle these challenges by proposing a novel model Self-Attention Adversarial Latent Autoencoder (SA-ALAE), which allows generating feasible design images of complex engineering parts. With SA-ALAE, users can not only explore novel variants of an existing design, but also control the generation process by operating in latent space. The potential of SA-ALAE is shown by generating engineering blueprints in a real automotive design task.

翻译：基于深度生成模型的生成式工程设计方法已被提出以促进工业工程流程。在此类流程中，设计通常以图像形式呈现，例如根据详细程度不同可分为蓝图、工程图纸和CAD模型。深度生成模型已成功应用于自然图像的合成，例如展示动物、人脸和风景的图像。然而，工业设计图像与自然场景存在本质差异，其包含丰富的结构模式和长程依赖关系，这对基于卷积的深度生成模型的生成能力提出了挑战。此外，基于深度生成模型的生成过程通常由随机噪声输入触发，其输出样本具有不可预测性，因而无法实现高效的工业设计探索。为应对这些挑战，我们提出了一种新颖的模型——自注意力对抗性潜在自编码器，该模型能够生成复杂工程零件的可行设计图像。通过SA-ALAE，用户不仅能够探索现有设计的新变体，还能通过在潜在空间中进行操作来控制生成过程。通过在真实汽车设计任务中生成工程蓝图，展示了SA-ALAE的潜力。