# Generating Modern Persian Carpet Map by Style-transfer

arXiv: https://arxiv.org/abs/2308.04529 · HTML: https://mlsanity.com/paper/2308.04529/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 15 of its 23 references that are in the map.
- First posted on arXiv 8 Aug 2023.

## Builds on (most cited first, 15 of 15)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [A Style-Based Generator Architecture for Generative Adversarial Networks](https://mlsanity.com/paper/1812.04948/) (2018)
- [Visual Genome: Connecting Language and Vision Using Crowdsourced Dense Image Annotations](https://mlsanity.com/paper/1602.07332/) (2016)
- [Arbitrary Style Transfer in Real-time with Adaptive Instance Normalization](https://mlsanity.com/paper/1703.06868/) (2017)
- [Universal Style Transfer via Feature Transforms](https://mlsanity.com/paper/1705.08086/) (2017)
- [CLIPstyler: Image Style Transfer with a Single Text Condition](https://mlsanity.com/paper/2112.00374/) (2021)
- [Avatar-Net: Multi-scale Zero-shot Style Transfer by Feature Decoration](https://mlsanity.com/paper/1805.03857/) (2018)
- [Combining Markov Random Fields and Convolutional Neural Networks for Image Synthesis](https://mlsanity.com/paper/1601.04589/) (2016)
- [Domain Enhanced Arbitrary Image Style Transfer via Contrastive Learning](https://mlsanity.com/paper/2205.09542/) (2022)
- [Fast Patch-based Style Transfer of Arbitrary Style](https://mlsanity.com/paper/1612.04337/) (2016)
- [Arbitrary Style Transfer with Deep Feature Reshuffle](https://mlsanity.com/paper/1805.04103/) (2018)
- [Multimodal Style Transfer via Graph Cuts](https://mlsanity.com/paper/1904.04443/) (2019)
- [Name Your Style: An Arbitrary Artist-aware Image Style Transfer](https://mlsanity.com/paper/2202.13562/) (2022)
- [CAMS: Color-Aware Multi-Style Transfer](https://mlsanity.com/paper/2106.13920/) (2021)

## Abstract (arXiv)

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.