  Fashion manipulation has attracted growing interest due to its great
application value, which inspires many researches towards fashion images.
However, little attention has been paid to fashion design draft. In this paper,
we study a new unaligned translation problem between design drafts and real
fashion items, whose main challenge lies in the huge misalignment between the
two modalities. We first collect paired design drafts and real fashion item
images without pixel-wise alignment. To solve the misalignment problem, our
main idea is to train a sampling network to adaptively adjust the input to an
intermediate state with structure alignment to the output. Moreover, built upon
the sampling network, we present design draft to real fashion item translation
network (D2RNet), where two separate translation streams that focus on texture
and shape, respectively, are combined tactfully to get both benefits. D2RNet is
able to generate realistic garments with both texture and shape consistency to
their design drafts. We show that this idea can be effectively applied to the
reverse translation problem and present R2DNet accordingly. Extensive
experiments on unaligned fashion design translation demonstrate the superiority
of our method over state-of-the-art methods. Our project website is available
at: https://victoriahy.github.io/MM2020/ .
