AnimateAnything: Consistent and Controllable Animation for Video
  Generation
Authors: name: Guojun Lei, name: Chi Wang, name: Hong Li, name: Rong Zhang, name: Yikai Wang, name: Weiwei Xu

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/.