  Image editing has been a long-standing challenge in the research community
with its far-reaching impact on numerous applications. Recently, text-driven
methods started to deliver promising results in domains like human faces, but
their applications to more complex domains have been relatively limited. In
this work, we explore the task of fashion style editing, where we aim to
manipulate the fashion style of human imagery using text descriptions.
Specifically, we leverage a generative human prior and achieve fashion style
editing by navigating its learned latent space. We first verify that the
existing text-driven editing methods fall short for our problem due to their
overly simplified guidance signal, and propose two directions to reinforce the
guidance: textual augmentation and visual referencing. Combined with our
empirical findings on the latent space structure, our Fashion Style Editing
framework (FaSE) successfully projects abstract fashion concepts onto human
images and introduces exciting new applications to the field.
