  Image-based virtual try-on is one of the most promising applications of
human-centric image generation due to its tremendous real-world potential. Yet,
as most try-on approaches fit in-shop garments onto a target person, they
require the laborious and restrictive construction of a paired training
dataset, severely limiting their scalability. While a few recent works attempt
to transfer garments directly from one person to another, alleviating the need
to collect paired datasets, their performance is impacted by the lack of paired
(supervised) information. In particular, disentangling style and spatial
information of the garment becomes a challenge, which existing methods either
address by requiring auxiliary data or extensive online optimization
procedures, thereby still inhibiting their scalability. To achieve a
\emph{scalable} virtual try-on system that can transfer arbitrary garments
between a source and a target person in an unsupervised manner, we thus propose
a texture-preserving end-to-end network, the PAtch-routed SpaTially-Adaptive
GAN (PASTA-GAN), that facilitates real-world unpaired virtual try-on.
Specifically, to disentangle the style and spatial information of each garment,
PASTA-GAN consists of an innovative patch-routed disentanglement module for
successfully retaining garment texture and shape characteristics. Guided by the
source person keypoints, the patch-routed disentanglement module first
decouples garments into normalized patches, thus eliminating the inherent
spatial information of the garment, and then reconstructs the normalized
patches to the warped garment complying with the target person pose. Given the
warped garment, PASTA-GAN further introduces novel spatially-adaptive residual
blocks that guide the generator to synthesize more realistic garment details.
