Neural Face Reenactment

Neural face reenactment aims to realistically transfer facial expressions and poses from one video or image (the target) onto another (the source), preserving the source's identity. Recent research heavily utilizes generative adversarial networks (GANs), diffusion models, and neural radiance fields (NeRFs), often focusing on improving one-shot reenactment (using a single source image) and addressing challenges like scale discrepancies and accurate expression transfer. These advancements are significant for applications in video conferencing, animation, and even forensic analysis, offering improved realism and efficiency in generating synthetic facial videos.