Face Video Editing

Face video editing aims to modify facial attributes in videos while preserving identity and temporal consistency, a challenging task due to the complex interplay of appearance and motion. Recent research focuses on developing models that disentangle identity and expression features, often employing techniques like sparse optimization, dynamic neural radiance fields (NeRFs), and diffusion autoencoders, to achieve high-fidelity edits across multiple views and frames. These advancements are significant for applications ranging from film post-production to virtual reality, offering improved control and realism in manipulating facial features within video sequences. The field is actively exploring methods that improve both the quality and the physical interpretability of edits, moving beyond simple latent space manipulation.