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/.
@article{arxiv.2312.03775,
title = {FAAC: Facial Animation Generation with Anchor Frame and Conditional Control for Superior Fidelity and Editability},
author = {Linze Li and Sunqi Fan and Hengjun Pu and Zhaodong Bing and Yao Tang and Tianzhu Ye and Tong Yang and Liangyu Chen and Jiajun Liang},
journal= {arXiv preprint arXiv:2312.03775},
year = {2023}
}