Handloom Design Generation Using Generative Networks
This addresses design automation for handloom clothing, but it is incremental as it applies existing methods to a new domain.
The paper tackled generating handloom fabric designs using generative neural networks and style transfer algorithms, resulting in a new dataset NeuralLoom and evaluation via user scores.
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.