Abstract
This study presents WoolGAN, a lightweight texture style transfer method based on a generative adversarial network (GAN), with wool felting texture as the primary example. Unlike conventional convolutional approaches, it requires only a small training dataset of approximately 300 images and is capable of preserving the shape of the target object in the image. To achieve this, color hints and edge maps with background separation are used as inputs during both training and generation phases. Experimental results demonstrate that the generated images are highly realistic and well-received by human evaluators. Moreover, this method can be broadly applied to other texture styles, especially when only limited datasets are available.