  The success of Deep Generative Models at high-resolution image generation has
led to their extensive utilization for style editing of real images. Most
existing methods work on the principle of inverting real images onto their
latent space, followed by determining controllable directions. Both inversion
of real images and determination of controllable latent directions are
computationally expensive operations. Moreover, the determination of
controllable latent directions requires additional human supervision. This work
aims to explore the efficacy of mask-guided feature modulation in the latent
space of a Deep Generative Model as a solution to these bottlenecks. To this
end, we present the SemanticStyle Autoencoder (SSAE), a deep Generative
Autoencoder model that leverages semantic mask-guided latent space manipulation
for highly localized photorealistic style editing of real images. We present
qualitative and quantitative results for the same and their analysis. This work
shall serve as a guiding primer for future work.
