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Over recent years, diffusion models have facilitated significant advancementsin video generation. Yet, the creation of face-related videos still confrontsissues such as low facial fidelity, lack of frame consistency, limitededitability and uncontrollable human poses. To address these challenges, weintroduce a facial animation generation method that enhances both face identityfidelity and editing capabilities while ensuring frame consistency. Thisapproach incorporates the concept of an anchor frame to counteract thedegradation of generative ability in original text-to-image models whenincorporating a motion module. We propose two strategies towards thisobjective: training-free and training-based anchor frame methods. Our method'sefficacy has been validated on multiple representative DreamBooth and LoRAmodels, delivering substantial improvements over the original outcomes in termsof facial fidelity, text-to-image editability, and video motion. Moreover, weintroduce conditional control using a 3D parametric face model to captureaccurate facial movements and expressions. This solution augments the creativepossibilities for facial animation generation through the integration ofmultiple control signals. For additional samples, please visithttps://paper-faac.github.io/.

FAAC: Facial Animation Generation with Anchor Frame and Conditional Control for Superior Fidelity and Editability