Animate Anyone aims to generate character video from still images by driving signals. Using the power of diffusion model, we put forward a new framework tailored for character animation. In order to keep the consistency of complex appearance features in reference images, we designed ReferenceNet to merge detailed features through spatial attention. In order to ensure controllability and continuity, we introduce an efficient posture director to guide the actions of the characters, and adopt an effective time modeling method to ensure a smooth cross-frame transition between video frames. By expanding the training data, our method can animate any character, and compared with other image-to-video methods, it has achieved excellent results in character animation. In addition, we evaluated our method on the basis of fashion video and human dance synthesis, and achieved the most advanced results.
Demand crowd:
“Used to convert static images into character videos, especially suitable for fashion video synthesis and human dance generation”
Examples of usage scenarios:
Transform fashion photos into realistic animated videos with Animate Anyone.
Using Animate Anyone to generate human dance on TikTok dataset
Making Animation Video for Animate/Cartoon Characters with Animate Anyone
Product features:
Generating a character video from a still image by a driving signal
Using the power of diffusion model
ReferenceNet is designed to merge detailed features through spatial attention.
An efficient posture director is introduced to guide the actions of the characters.
Effective temporal modeling method is adopted to ensure smooth cross-frame transition between video frames.
Expand the training data, so that the method can make animation for any role.
Get the most advanced results on the benchmark of fashion video and human dance synthesis.