  Deep generative models are known to be able to model arbitrary probability
distributions. Among these, a recent deep generative model, dubbed sliceGAN,
proposed a new way of using the generative adversarial network (GAN) to capture
the micro-structural characteristics of a two-dimensional (2D) slice and
generate three-dimensional (3D) volumes with similar properties. While 3D
micrographs are largely beneficial in simulating diverse material behavior,
they are often much harder to obtain than their 2D counterparts. Hence,
sliceGAN opens up many interesting directions of research by learning the
representative distribution from 2D slices, and transferring the learned
knowledge to generate arbitrary 3D volumes. However, one limitation of sliceGAN
is that latent space steering is not possible. Hence, we combine sliceGAN with
AdaIN to endow the model with the ability to disentangle the features and
control the synthesis.
