  Semantic image synthesis is a process for generating photorealistic images
from a single semantic mask. To enrich the diversity of multimodal image
synthesis, previous methods have controlled the global appearance of an output
image by learning a single latent space. However, a single latent code is often
insufficient for capturing various object styles because object appearance
depends on multiple factors. To handle individual factors that determine object
styles, we propose a class- and layer-wise extension to the variational
autoencoder (VAE) framework that allows flexible control over each object class
at the local to global levels by learning multiple latent spaces. Furthermore,
we demonstrate that our method generates images that are both plausible and
more diverse compared to state-of-the-art methods via extensive experiments
with real and synthetic datasets inthree different domains. We also show that
our method enables a wide range of applications in image synthesis and editing
tasks.
