Jamdani Motif Generation using Conditional GAN

TL;DR

This paper presents a novel GAN-based technique for generating new Jamdani textile motifs from user-input sketches, leveraging a unique dataset of authentic motifs and the pix2pix model to produce realistic designs.

Contribution

It introduces the first digital dataset of Jamdani motifs and applies a conditional GAN to generate authentic-looking patterns from rough sketches.

Findings

Generated motifs closely resemble authentic Jamdani designs

The pix2pix model effectively translates sketches into detailed patterns

The dataset enables future research in digital textile design

Abstract

Jamdani is the strikingly patterned textile heritage of Bangladesh. The exclusive geometric motifs woven on the fabric are the most attractive part of this craftsmanship having a remarkable influence on textile and fine art. In this paper, we have developed a technique based on the Generative Adversarial Network that can learn to generate entirely new Jamdani patterns from a collection of Jamdani motifs that we assembled, the newly formed motifs can mimic the appearance of the original designs. Users can input the skeleton of a desired pattern in terms of rough strokes and our system finalizes the input by generating the complete motif which follows the geometric structure of real Jamdani ones. To serve this purpose, we collected and preprocessed a dataset containing a large number of Jamdani motifs images from authentic sources via fieldwork and applied a state-of-the-art method called pix2pix to it. To the best of our knowledge, this dataset is currently the only available dataset of Jamdani motifs in digital format for computer vision research. Our experimental results of the pix2pix model on this dataset show satisfactory outputs of computer-generated images of Jamdani motifs and we believe that our work will open a new avenue for further research.