This web content introduces a research paper titled “Animate Anyone: Consistent and Controllable Image-to-Video Synthesis for Character Animation.” The paper proposed a novel framework for character animation, leveraging diffusion models to maintain consistency and control in image-to-video synthesis. The ReferenceNet was designed to merge detailed features from a reference image, while an efficient pose guider directed the character’s movements. The method also employed temporal modeling for smooth inter-frame transitions. By expanding the training data, the approach was able to animate arbitrary characters and achieved superior results compared to other image-to-video methods. The paper was evaluated on fashion video and human dance synthesis benchmarks, where it achieved state-of-the-art results.
https://humanaigc.github.io/animate-anyone/