  Virtual fitting room is a challenging task yet useful feature for e-commerce
platforms and fashion designers. Existing works can only detect very few types
of fashion items. Besides they did poorly in changing the texture and style of
the selected fashion items. In this project, we propose a novel approach to
address this problem. We firstly used Mask R-CNN to find the regions of
different fashion items, and secondly used Neural Style Transfer to change the
style of the selected fashion items. The dataset we used is composed of images
from PaperDoll dataset and annotations provided by eBay's ModaNet. We trained 8
models and our best model massively outperformed baseline models both
quantitatively and qualitatively, with 68.72% mAP, 0.2% ASDR.
