  TryOnGAN is a recent virtual try-on approach, which generates highly
realistic images and outperforms most previous approaches. In this article, we
reproduce the TryOnGAN implementation and probe it along diverse angles: impact
of transfer learning, variants of conditioning image generation with poses and
properties of latent space interpolation. Some of these facets have never been
explored in literature earlier. We find that transfer helps training initially
but gains are lost as models train longer and pose conditioning via
concatenation performs better. The latent space self-disentangles the pose and
the style features and enables style transfer across poses. Our code and models
are available in open source.
