AnimateAnything: Consistent and Controllable Animation for Video
  Generation
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/.
673c2cebabddf849496a677e
Guojun Lei
673c2cebabddf849496a677f
Chi Wang
673c2cebabddf849496a6780
Hong Li
673c2cebabddf849496a6781
Rong Zhang
673c2cebabddf849496a6782
Yikai Wang
extracted_confirmed
2024-11-19T06:25:38.876Z
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/avatars/a8f830d1c47702f642045399e59c9fe8.svg
Yikai Wang
user
yikaiw
673c2cebabddf849496a6783
Weiwei Xu
AnimateAnything: Consistent and Controllable Animation for Video
  Generation
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/.
A unified controllable video generation method AnimateAnything utilizes a multi-scale fusion network and optical flows to handle various conditions, achieving high consistency and stability in generated videos.
multi-scale control feature fusion network frame-by-frame optical flows motion priors frequency-based stabilization module temporal coherence frequency domain consistency