Face Synthesis

Face synthesis research aims to generate realistic and controllable facial images and videos, often driven by various inputs like audio, text, or other images. Current efforts focus on improving realism and control using advanced architectures such as Generative Adversarial Networks (GANs), diffusion models, and Neural Radiance Fields (NeRFs), often incorporating techniques like disentanglement of facial attributes and multi-modal conditioning. This field is significant due to its applications in areas like virtual reality, animation, and security, while also raising ethical concerns regarding the potential for misuse in creating deepfakes and impacting human perception.