Footwear Sketches Colorization Method based on Generative Adversarial Network

Abstract

The coloring of sketches has a constant market demand in the area of research. The difficulty of the coloring sketch outline is its lack of texture and color. Take footwear design as an example, it is difficult for designers to complete a colorful sketch in a limited time, so an artificial intelligence technology for coloring shoes is required. Though we do not build a new GAN, which is based on pix2pix. We try to integrate the existing model in four ways, including generator, discriminator, loss function and comparison. In this paper, given a set of edges-to-shoes that have 50,025 shoe images, our approach produces an image with vivid shoes images. Unlike the recent research, our approach is not based on a unique adversarial training. We show that shoe sketches can be synthesized from simple lines by a GAN into a high-resolution picture. In particular, we offer a new model to synthesize high-resolution photo-realistic images of shoes, and apply a multi-discriminator to train and distinguish the generated images. Our model enables the shoe designer to benefit from the colorization design.