  Image virtual try-on task has abundant applications and has become a hot
research topic recently. Existing 2D image-based virtual try-on methods aim to
transfer a target clothing image onto a reference person, which has two main
disadvantages: cannot control the size and length precisely; unable to
accurately estimate the user's figure in the case of users wearing thick
clothes, resulting in inaccurate dressing effect. In this paper, we put forward
an akin task that aims to dress clothing for underwear models. %, which is also
an urgent need in e-commerce scenarios. To solve the above drawbacks, we
propose a Shape Controllable Virtual Try-On Network (SC-VTON), where a graph
attention network integrates the information of model and clothing to generate
the warped clothing image. In addition, the control points are incorporated
into SC-VTON for the desired clothing shape. Furthermore, by adding a Splitting
Network and a Synthesis Network, we can use clothing/model pair data to help
optimize the deformation module and generalize the task to the typical virtual
try-on task. Extensive experiments show that the proposed method can achieve
accurate shape control. Meanwhile, compared with other methods, our method can
generate high-resolution results with detailed textures.
