High Fidelity Face Generation with Style Generative Adversarial Networks

This study proposes a novel Generative Adversarial Network (GAN)-based model for text-to-face generation, which generates high-fidelity, diverse and realistic facial images that are consistent with the provided textual descriptions. The model utilizes the StyleGAN architecture and is trained on a large dataset of real human faces. Various metrics are employed to assess the effectiveness of the proposed model. The potential applications of this technology are diverse and include criminal investigation, data augmentation for face recognition systems, computer graphics, and entertainment. The proposed model can generate realistic and visually appealing facial images that can be useful in a variety of fields. This study demonstrates the effectiveness of using GAN-based models for text-to-image generation, highlighting their potential for improving the accuracy and robustness of various applications.