Guiding the Latent Space of Generative Models based on Subjective Human Factors

Abstract

Generative Artificial intelligence (AI) specifically Deep Generative Models (DGMs) has shown significant potential for generating engineering product designs. While prior research demonstrates DGMs’ ability to incorporate performance objectives, many creative design challenges lack well-defined objective functions. This paper explores the relationship between key physical parameters of bicycles and their usability, aiming to guide a DGM in generating or enhancing designs based on usability. A crowdsourced dataset of bicycle ratings is gathered and used to train a Support Vector Machine (SVM), achieving 95\% test accuracy, to identify critical parameter relationships. The SVM identifies three key parameters that effectively determine a bicycle's usability, namely: Saddle Height, Stack, and CS Textfield. Additionally, Kernel Density Estimation (KDE) with Gaussian kernels is applied in the latent space of a Variational Autoencoder (VAE) to model the usability labels. These KDEs provide continuous log-likelihood scores across the latent space, enabling the classification of each latent point. While latent classification achieves 75\% test accuracy, usability enhancements to the bicycle are effective, as validated by the high-accuracy SVM model. This method of implicitly capturing parameter relationships with usability mitigates the need for exhaustive testing when attempting to uncover them explicitly. Furthermore, KDE guidance in the latent space offers designers precise control over the extent of changes made to an original design, while guided interpolation provides the flexibility to introduce a second design to act as inspiration, enabling the exploration of the design space between them.