AnimateAnything: Consistent and Controllable Animation for Video Generation
Audio is streamed directly from the publisher (media.transistor.fm) as published in their RSS feed. Play Podcasts does not host this file. Rights-holders can request removal through the copyright & takedown page.
Show Notes
🤗 Paper Upvotes: 12 | cs.CV
Authors:
Guojun Lei, Chi Wang, Hong Li, Rong Zhang, Yikai Wang, Weiwei Xu
Title:
AnimateAnything: Consistent and Controllable Animation for Video Generation
Arxiv:
http://arxiv.org/abs/2411.10836v1
Abstract:
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/.