  2D image-based virtual try-on has attracted increased attention from the
multimedia and computer vision communities. However, most of the existing
image-based virtual try-on methods directly put both person and the in-shop
clothing representations together, without considering the mutual correlation
between them. What is more, the long-range information, which is crucial for
generating globally consistent results, is also hard to be established via the
regular convolution operation. To alleviate these two problems, in this paper
we propose a novel two-stage Cloth Interactive Transformer (CIT) for virtual
try-on. In the first stage, we design a CIT matching block, aiming to perform a
learnable thin-plate spline transformation that can capture more reasonable
long-range relation. As a result, the warped in-shop clothing looks more
natural. In the second stage, we propose a novel CIT reasoning block for
establishing the global mutual interactive dependence. Based on this mutual
dependence, the significant region within the input data can be highlighted,
and consequently, the try-on results can become more realistic. Extensive
experiments on a public fashion dataset demonstrate that our CIT can achieve
the new state-of-the-art virtual try-on performance both qualitatively and
quantitatively. The source code and trained models are available at
https://github.com/Amazingren/CIT.
