title: AnimateAnything: Consistent and Controllable Animation for Video
    Generation
summary: 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/.
paper_authors._id: 673c2cebabddf849496a677e
paper_authors.name: Guojun Lei
paper_authors._id: 673c2cebabddf849496a677f
paper_authors.name: Chi Wang
paper_authors._id: 673c2cebabddf849496a6780
paper_authors.name: Hong Li
paper_authors._id: 673c2cebabddf849496a6781
paper_authors.name: Rong Zhang
paper_authors._id: 673c2cebabddf849496a6782
paper_authors.name: Yikai Wang
paper_authors.status: extracted_confirmed
paper_authors.statusLastChangedAt: 2024-11-19T06:25:38.876Z
paper_authors._id: 64206b35e40f66bcd1e3c985
paper_authors.avatarUrl: /avatars/a8f830d1c47702f642045399e59c9fe8.svg
paper_authors.fullname: Yikai Wang
paper_authors.type: user
paper_authors.user: yikaiw
paper_authors._id: 673c2cebabddf849496a6783
paper_authors.name: Weiwei Xu
paper_title: AnimateAnything: Consistent and Controllable Animation for Video
    Generation
paper_summary: 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/.
paper_ai_summary: 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.
paper_ai_keywords: multi-scale control feature fusion network frame-by-frame optical flows motion priors frequency-based stabilization module temporal coherence frequency domain consistency