Handloom Design Generation Using Generative Networks

TL;DR

This paper explores deep learning methods, including generative models and style transfer, for creating handloom fabric designs, introduces a new dataset, and evaluates performance through user scores.

Contribution

It introduces NeuralLoom, a new dataset for handloom design generation, and evaluates multiple generative approaches for artistic fabric design synthesis.

Findings

Generative models can effectively synthesize handloom designs.

Style transfer algorithms show promising results in fabric design generation.

User evaluations favor certain models for design quality.

Abstract

This paper proposes deep learning techniques of generating designs for clothing, focused on handloom fabric and discusses the associated challenges along with its application. The capability of generative neural network models in understanding artistic designs and synthesizing those is not yet explored well. In this work, multiple methods are employed incorporating the current state of the art generative models and style transfer algorithms to study and observe their performance for the task. The results are then evaluated through user score. This work also provides a new dataset NeuralLoom for the task of the design generation.