ONE SHOT FACE STYLIZATION USING GANS

Abstract

One-shot face stylization is an interesting and challenging subject in computer vision and deep learning. This work deals with the art of manipulating a target face using a reference image as inspiration, which requires controlling facial recognition while specifying important style characteristics This project has attracted a lot of interest due to its potential applications in digital art, entertainment, and personal products. In this abstract, we examine the important features of a one- shot face stylization. Deep neural networks, especially generative adversarial networks (GANs), are widely used in the process to generate customized facial images. These networks are trained on data structures that combine the target and reference faces, with the reference image acting as a strategic identifier. The success of a one-shot facial lies in the meticulous execution of the fading process, which strikes a balance between preserving identity and improving technique. These disadvantages typically include a combination of manpower retention, strategic formation, emotional quality, and enemy training. In conclusion, advances in this field have the potential to transform creative expression and personalization across industries from digital art and animation to virtual avatars and social media filters. Key Words: Facial recognition, generative adversarial networks, virtual avatar, image to image transfer.