Abstract. Face generation, as a cutting-edge generative technology, has made significant strides in various image synthesis tasks, including facial inpainting, text-to-image translation, and video-based facial animation. Generating realistic and diverse human faces is a critical task in computer vision, with a wide array of real-world applications. Given the remarkable success of face generation models, there has been a growing interest in leveraging advanced techniques to further improve the quality, diversity, and control of generated faces. This paper will provide a comprehensive overview of the state-of-the-art face generation techniques. Specifically, the paper reviews the key approaches in this field, including Generative Adversarial Networks (GANs), Vector Quantized methods, and the more recent Diffusion Models, each of which has contributed significantly to the advancement of face generation. The paper discusses how these models have evolved to handle the complexity of face generation, from capturing subtle facial details to enabling fine-grained control over facial attributes. The paper also explores the major applications of face generation technologies, with a particular emphasis on their use in entertainment, virtual reality, and security. Finally, the paper identifies promising directions for future research in face generation, such as improving the interpretability of models, addressing ethical concerns, and enhancing the ability to generate faces that are both highly realistic and diverse.
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