  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.
