  Image animation is a key task in computer vision which aims to generate
dynamic visual content from static image. Recent image animation methods employ
neural based rendering technique to generate realistic animations. Despite
these advancements, achieving fine-grained and controllable image animation
guided by text remains challenging, particularly for open-domain images
captured in diverse real environments. In this paper, we introduce an open
domain image animation method that leverages the motion prior of video
diffusion model. Our approach introduces targeted motion area guidance and
motion strength guidance, enabling precise control the movable area and its
motion speed. This results in enhanced alignment between the animated visual
elements and the prompting text, thereby facilitating a fine-grained and
interactive animation generation process for intricate motion sequences. We
validate the effectiveness of our method through rigorous experiments on an
open-domain dataset, with the results showcasing its superior performance.
Project page can be found at https://animationai.github.io/AnimateAnything.
