cGAN-Based Garment Line Draft Colorization Using a Garment-Line Dataset

Garment line draft is the basis of clothing design. Automatic or semi-automatic colorization of garment line draft will improve the efficiency of fashion designers and reduce the drawing cost. In this paper, we present a garment line draft colorization method based on cGAN, which can support user interaction by adding scribbles to guide the colorization process. Due to the inadequacy of the garment line drafts, we construct a paired garment-line image dataset for training our colorization model. While existing methods for line art colorization are able to generate plausible colorized results, they tend to suffer from the color bleeding issue. We introduce a region segmentation fusion mechanism to aid colorization frameworks in avoiding color bleeding. The experimental results show that each module in the method can contribute to the final result. In addition, the comparison with the classical methods that our method can avoid large areas of leakage in the background and have cleaner garment details.