Deep face generation from a rough sketch using multi-level generative adversarial networks
Binghua Xie, Cheolkon Jung · 2022 26th International Conference on Pattern Recognition (ICPR) · 2022
Face generation from a rough sketch is a challenging task in computer vision, and most methods are based on conditional generative adversarial networks (cGANs) that use U-net as their backbone. They are helped by auxiliary information such as segmentation and parsing maps to generate visually pleasing facial images from sketches. However, it is hard to get the auxiliary information in reality when the input is only a rough sketch. In this paper, we propose deep face generation from a rough sketch using multi-level GANs. The proposed method does not need any auxiliary information for face generation except the input rough sketch. Instead, we generate a face image by the preceding generator and use it as auxiliary information for the succeeding generator along with the input sketch. The multilevel GANs progressively generates fine textures and contours in face images, thus leading to photo-realistic face components and hair even from a rough sketch. Various experiments show that the proposed method generates natural-looking face images and outperforms state-of-the-art methods in terms of visual quality and quantitative measurements.