Text Guided 3D Face

Text-guided 3D face generation and manipulation aims to create and modify three-dimensional facial models based on textual descriptions, enabling precise control over facial features and attributes. Current research focuses on improving the efficiency and quality of these processes, employing techniques like diffusion models, generative adversarial networks (GANs), and neural radiance fields (NeRFs) to achieve realistic and semantically consistent results, often incorporating cross-modal alignment strategies. This field is significant for its potential applications in various domains, including virtual reality, animation, and personalized avatar creation, driving advancements in both computer graphics and artificial intelligence.