ABSTRACT:

This paper explores deep learning techniques for generating clothing designs focused on handloom fabrics, addressing associated challenges and applications. The potential of generative neural network models in understanding and synthesizing artistic designs has not been thoroughly investigated. We employ various state-of-the-art generative models and style transfer algorithms to evaluate their performance in design generation tasks. The effectiveness of these methods is assessed through user scoring. Additionally, we introduce a novel dataset, "Neural Loom," specifically curated for design generation in the context of handloom textiles. Our findings contribute to the intersection of fashion and artificial intelligence, providing insights into the capabilities and limitations of current generative models in the domain of textile design.

PROBLEM STATEMENT:

The traditional handloom textile industry faces a decline in relevance due to the limited innovation in design, which is often constrained by cultural biases and the challenges of merging distinct design styles. This paper aims to address the problem of generating innovative clothing designs that incorporate both traditional handloom elements and contemporary aesthetics. Specifically, we seek to develop and evaluate deep learning techniques that facilitate the transfer and generation of design textures, allowing for the creation of unique and appealing handloom patterns. By leveraging generative neural networks, we aim to overcome the difficulties of quantifying and integrating diverse design influences, ultimately contributing to the revival and modernization of handloom textiles.

OBJECTIVE
The primary objective of this project is to develop and evaluate deep learning techniques for the generation of innovative clothing designs that integrate traditional handloom aesthetics with contemporary styles. This involves:
1. Design Generation: To create new, unique patterns and textures for handloom textiles using generative models, specifically focusing on GANs and style transfer algorithms.
2. Dataset Development: To curate and introduce a comprehensive dataset, "NeuralLoom," comprising diverse handloom designs, which serves as a foundation for training generative models.
3. Technique Evaluation: To assess the effectiveness of various deep learning methodologies—including texture transfer and design fusion—through user acceptance tests and comparative analysis of generated designs.
4. Application Development: To implement a user-friendly web application that showcases the capabilities of the developed techniques, allowing designers and researchers to explore and utilize AI-generated handloom designs.