  Deep learning based virtual try-on system has achieved some encouraging
progress recently, but there still remain several big challenges that need to
be solved, such as trying on arbitrary clothes of all types, trying on the
clothes from one category to another and generating image-realistic results
with few artifacts. To handle this issue, we in this paper first collect a new
dataset with all types of clothes, \ie tops, bottoms, and whole clothes, each
one has multiple categories with rich information of clothing characteristics
such as patterns, logos, and other details. Based on this dataset, we then
propose the Arbitrary Virtual Try-On Network (AVTON) that is utilized for
all-type clothes, which can synthesize realistic try-on images by preserving
and trading off characteristics of the target clothes and the reference person.
Our approach includes three modules: 1) Limbs Prediction Module, which is
utilized for predicting the human body parts by preserving the characteristics
of the reference person. This is especially good for handling cross-category
try-on task (\eg long sleeves \(\leftrightarrow\) short sleeves or long pants
\(\leftrightarrow\) skirts, \etc), where the exposed arms or legs with the skin
colors and details can be reasonably predicted; 2) Improved Geometric Matching
Module, which is designed to warp clothes according to the geometry of the
target person. We improve the TPS based warping method with a compactly
supported radial function (Wendland's \(\Psi\)-function); 3) Trade-Off Fusion
Module, which is to trade off the characteristics of the warped clothes and the
reference person. This module is to make the generated try-on images look more
natural and realistic based on a fine-tune symmetry of the network structure.
Extensive simulations are conducted and our approach can achieve better
performance compared with the state-of-the-art virtual try-on methods.
