Animate Anyone aims to generate character videos from static images driven by signals. Leveraging the power of diffusion models, we propose a novel framework tailored for character animation. To maintain consistency of complex appearance features present in the reference image, we design ReferenceNet to merge detailed features via spatial attention. To ensure controllability and continuity, we introduce an efficient pose guidance module to direct character movements and adopt an effective temporal modeling approach to ensure smooth cross-frame transitions between video frames. By extending the training data, our method can animate any character, achieving superior results in character animation compared to other image-to-video approaches. Moreover, we evaluate our method on benchmarks for fashion video and human dance synthesis, achieving state-of-the-art results.