  With the metaverse slowly becoming a reality and given the rapid pace of
developments toward the creation of digital humans, the need for a principled
style editing pipeline for human faces is bound to increase manifold. We cater
to this need by introducing the Latents2Semantics Autoencoder (L2SAE), a
Generative Autoencoder model that facilitates highly localized editing of style
attributes of several Regions of Interest (ROIs) in face images. The L2SAE
learns separate latent representations for encoded images' structure and style
information. Thus, allowing for structure-preserving style editing of the
chosen ROIs. The encoded structure representation is a multichannel 2D tensor
with reduced spatial dimensions, which captures both local and global structure
properties. The style representation is a 1D tensor that captures global style
attributes. In our framework, we slice the structure representation to build
strong and disentangled correspondences with different ROIs. Consequentially,
style editing of the chosen ROIs amounts to a simple combination of (a) the
ROI-mask generated from the sliced structure representation and (b) the decoded
image with global style changes, generated from the manipulated (using Gaussian
noise) global style and unchanged structure tensor. Style editing sans
additional human supervision is a significant win over SOTA style editing
pipelines because most existing works require additional human effort
(supervision) post-training for attributing semantic meaning to style edits. We
also do away with iterative-optimization-based inversion or determining
controllable latent directions post-training, which requires additional
computationally expensive operations. We provide qualitative and quantitative
results for the same over multiple applications, such as selective style
editing and swapping using test images sampled from several datasets.
