Text to Face

Text-to-face generation aims to create realistic facial images from textual descriptions, focusing on improving controllability, realism, and efficiency. Current research heavily utilizes Generative Adversarial Networks (GANs), particularly StyleGAN2, and diffusion models, often incorporating multimodal inputs like sketches or 3D face models to enhance control and detail. This field is significant for its applications in multimedia content creation, virtual and augmented reality, and other areas requiring realistic human face generation, driving advancements in image synthesis and understanding of human facial features.