  The virtual try-on task is so attractive that it has drawn considerable
attention in the field of computer vision. However, presenting the
three-dimensional (3D) physical characteristic (e.g., pleat and shadow) based
on a 2D image is very challenging. Although there have been several previous
studies on 2D-based virtual try-on work, most 1) required user-specified target
poses that are not user-friendly and may not be the best for the target
clothing, and 2) failed to address some problematic cases, including facial
details, clothing wrinkles and body occlusions. To address these two
challenges, in this paper, we propose an innovative template-free try-on image
synthesis (TF-TIS) network. The TF-TIS first synthesizes the target pose
according to the user-specified in-shop clothing. Afterward, given an in-shop
clothing image, a user image, and a synthesized pose, we propose a novel model
for synthesizing a human try-on image with the target clothing in the best
fitting pose. The qualitative and quantitative experiments both indicate that
the proposed TF-TIS outperforms the state-of-the-art methods, especially for
difficult cases.
