  Over recent years, diffusion models have facilitated significant advancements
in video generation. Yet, the creation of face-related videos still confronts
issues such as low facial fidelity, lack of frame consistency, limited
editability and uncontrollable human poses. To address these challenges, we
introduce a facial animation generation method that enhances both face identity
fidelity and editing capabilities while ensuring frame consistency. This
approach incorporates the concept of an anchor frame to counteract the
degradation of generative ability in original text-to-image models when
incorporating a motion module. We propose two strategies towards this
objective: training-free and training-based anchor frame methods. Our method's
efficacy has been validated on multiple representative DreamBooth and LoRA
models, delivering substantial improvements over the original outcomes in terms
of facial fidelity, text-to-image editability, and video motion. Moreover, we
introduce conditional control using a 3D parametric face model to capture
accurate facial movements and expressions. This solution augments the creative
possibilities for facial animation generation through the integration of
multiple control signals. For additional samples, please visit
https://paper-faac.github.io/.
