We present a unified controllable video generation approach AnimateAnything that facilitates precise and consistent video manipulation across various conditions, including camera trajectories, text prompts, and user motion annotations. Specifically, we carefully design a multi-scale control feature fusion network to construct a common motion representation for different conditions. It explicitly converts all control information into frame-by-frame optical flows. Then we incorporate the optical flows as motion priors to guide final video generation. In addition, to reduce the flickering issues caused by large-scale motion, we propose a frequency-based stabilization module. It can enhance temporal coherence by ensuring the video's frequency domain consistency. Experiments demonstrate that our method outperforms the state-of-the-art approaches. For more details and videos, please refer to the webpage: https://yu-shaonian.github.io/Animate_Anything/.
@article{arxiv.2411.10836,
title = {AnimateAnything: Consistent and Controllable Animation for Video Generation},
author = {Guojun Lei and Chi Wang and Hong Li and Rong Zhang and Yikai Wang and Weiwei Xu},
journal= {arXiv preprint arXiv:2411.10836},
year = {2024}
}