  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.
