Generating Modern Persian Carpet Map by Style-transfer
Dorsa Rahmatian Monireh Moshavash Mahdi Eftekhari Kamran Hoseinkhani
Today, the great performance of Deep Neural Networks(DNN) has been proven in
various fields. One of its most attractive applications is to produce artistic
designs. A carpet that is known as a piece of art is one of the most important
items in a house, which has many enthusiasts all over the world. The first
stage of producing a carpet is to prepare its map, which is a difficult,
time-consuming, and expensive task. In this research work, our purpose is to
use DNN for generating a Modern Persian Carpet Map. To reach this aim, three
different DNN style transfer methods are proposed and compared against each
other. In the proposed methods, the Style-Swap method is utilized to create the
initial carpet map, and in the following, to generate more diverse designs,
methods Clip-Styler, Gatys, and Style-Swap are used separately. In addition,
some methods are examined and introduced for coloring the produced carpet maps.
The designed maps are evaluated via the results of filled questionnaires where
the outcomes of user evaluations confirm the popularity of generated carpet
maps. Eventually, for the first time, intelligent methods are used in producing
carpet maps, and it reduces human intervention. The proposed methods can
successfully produce diverse carpet designs, and at a higher speed than
traditional ways.