  This paper proposes a novel garment transfer method supervised with knowledge
distillation from virtual try-on. Our method first reasons the transfer parsing
to provide shape prior to downstream tasks. We employ a multi-phase teaching
strategy to supervise the training of the transfer parsing reasoning model,
learning the response and feature knowledge from the try-on parsing reasoning
model. To correct the teaching error, it transfers the garment back to its
owner to absorb the hard knowledge in the self-study phase. Guided by the
transfer parsing, we adjust the position of the transferred garment via STN to
prevent distortion. Afterward, we estimate a progressive flow to precisely warp
the garment with shape and content correspondences. To ensure warping
rationality, we supervise the training of the garment warping model using
target shape and warping knowledge from virtual try-on. To better preserve body
features in the transfer result, we propose a well-designed training strategy
for the arm regrowth task to infer new exposure skin. Experiments demonstrate
that our method has state-of-the-art performance compared with other virtual
try-on and garment transfer methods in garment transfer, especially for
preserving garment texture and body features.
