  Image-based virtual try-on aims to fit an in-shop garment into a clothed
person image. To achieve this, a key step is garment warping which spatially
aligns the target garment with the corresponding body parts in the person
image. Prior methods typically adopt a local appearance flow estimation model.
They are thus intrinsically susceptible to difficult body poses/occlusions and
large mis-alignments between person and garment images (see
Fig.~\ref{fig:fig1}). To overcome this limitation, a novel global appearance
flow estimation model is proposed in this work. For the first time, a StyleGAN
based architecture is adopted for appearance flow estimation. This enables us
to take advantage of a global style vector to encode a whole-image context to
cope with the aforementioned challenges. To guide the StyleGAN flow generator
to pay more attention to local garment deformation, a flow refinement module is
introduced to add local context. Experiment results on a popular virtual try-on
benchmark show that our method achieves new state-of-the-art performance. It is
particularly effective in a `in-the-wild' application scenario where the
reference image is full-body resulting in a large mis-alignment with the
garment image (Fig.~\ref{fig:fig1} Top). Code is available at:
\url{https://github.com/SenHe/Flow-Style-VTON}.
