One central challenge in modeling material appearance perception is the creation of an explainable and navigable representation space. In this study, we address this by training a StyleGAN2-ADA deep generative model on a large-scale, physically based rendered dataset containing translucent and glossy objects with varying intrinsic optical parameters. The resulting latent vectors are analyzed through dimensionality reduction, and their perceptual validity is assessed via psychophysical experiments. Furthermore, we evaluate the generalization capabilities of StyleGAN2-ADA on unseen materials. We also explore inverse mapping techniques from latent vectors reduced by principal component analysis back to original optical parameters, highlighting both the potential and the limitations of generative models for explicit, parameter-based image synthesis. A comprehensive analysis provides significant insights into the latent structure of gloss and translucency perception and advances the practical application of generative models for controlled material appearance generation.