Fashion Designer Application Based on Generative Adversarial Network

Abstract

Embodying the design from scratches has been a common problem for all designers. In some situation, clothes designers have trouble creating works that match their idea the most. Creating a fine work from a simple sketch is a tedious process of endless try-and-error. Designers spend days, weeks, or even months drawing drafts and discard them, because they need to draw the picture that best embodies the idea in their head. This study proposes a deep learning method that can generate clothes images from human sketch input, explores the effect of different image to sketch translators, different Generative Adversarial Network (GAN) architecture, loss functions and regularization methods. Many previous works have demonstrated strong results on conditioned image generation, and this study shows that similar settings can be transferred to help designers to embody their designs. The result shows that the model can generate clothes images with high fidelity while faithful to the input sketch.