NAD: Neural Network Aided Design for Textile Pattern Generation

Textile pattern design is a challenging task that can be hardly resolved by a single deep neural network, due to the requirements on high resolution, periodic tiling, copyright protection and aesthetic preference of designers. In this paper, we present our NAD system which can automatically produce high-quality textile patterns for printing industry. Our NAD system splits the work into three steps: layout design, image filtering and pattern style transfer. In the first and last step, we employ different neural models to learn the process of artwork creation by human designers. Specifically, a reinforcement learning model is first developed for layout adjustment, followed by a CNN-based model for style transfer. We have employed our NAD system in an online production system with real customers and the results are very impressive and promising. The NAD system not only frees human designers from the labor intensive design process, but also results in a 2%-5% daily purchase rate.