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

AnimateAnything: Fine-Grained Open Domain Image Animation with Motion Guidance