Generating Modern Persian Carpet Map by Style-transfer

TL;DR

This paper explores the use of deep neural network style transfer techniques to generate modern Persian carpet maps, reducing human effort and increasing design diversity and speed.

Contribution

It introduces and compares three DNN style transfer methods for carpet map generation, a novel application in this domain, with user evaluations confirming effectiveness.

Findings

Generated carpet maps are diverse and popular according to user surveys.

Proposed methods outperform traditional manual design in speed.

First application of intelligent methods in Persian carpet map production.

Abstract

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.