  Prior work has extensively studied the latent space structure of GANs for
unconditional image synthesis, enabling global editing of generated images by
the unsupervised discovery of interpretable latent directions. However, the
discovery of latent directions for conditional GANs for semantic image
synthesis (SIS) has remained unexplored. In this work, we specifically focus on
addressing this gap. We propose a novel optimization method for finding
spatially disentangled class-specific directions in the latent space of
pretrained SIS models. We show that the latent directions found by our method
can effectively control the local appearance of semantic classes, e.g.,
changing their internal structure, texture or color independently from each
other. Visual inspection and quantitative evaluation of the discovered GAN
controls on various datasets demonstrate that our method discovers a diverse
set of unique and semantically meaningful latent directions for class-specific
edits.
