  Image-based virtual try-on for fashion has gained considerable attention
recently. The task requires trying on a clothing item on a target model image.
An efficient framework for this is composed of two stages: (1) warping
(transforming) the try-on cloth to align with the pose and shape of the target
model, and (2) a texture transfer module to seamlessly integrate the warped
try-on cloth onto the target model image. Existing methods suffer from
artifacts and distortions in their try-on output. In this work, we present
SieveNet, a framework for robust image-based virtual try-on. Firstly, we
introduce a multi-stage coarse-to-fine warping network to better model
fine-grained intricacies (while transforming the try-on cloth) and train it
with a novel perceptual geometric matching loss. Next, we introduce a try-on
cloth conditioned segmentation mask prior to improve the texture transfer
network. Finally, we also introduce a dueling triplet loss strategy for
training the texture translation network which further improves the quality of
the generated try-on results. We present extensive qualitative and quantitative
evaluations of each component of the proposed pipeline and show significant
performance improvements against the current state-of-the-art method.
