Character Animation aims to generating character videos from still images
through driving signals. Currently, diffusion models have become the mainstream
in visual generation research, owing to their robust generative capabilities.
However, challenges persist in the realm of image-to-video, especially in
character animation, where temporally maintaining consistency with detailed
information from character remains a formidable problem. In this paper, we
leverage the power of diffusion models and propose a novel framework tailored
for character animation. To preserve consistency of intricate appearance
features from reference image, we design ReferenceNet to merge detail features
via spatial attention. To ensure controllability and continuity, we introduce
an efficient pose guider to direct character's movements and employ an
effective temporal modeling approach to ensure smooth inter-frame transitions
between video frames. By expanding the training data, our approach can animate
arbitrary characters, yielding superior results in character animation compared
to other image-to-video methods. Furthermore, we evaluate our method on
benchmarks for fashion video and human dance synthesis, achieving
state-of-the-art results.

通过扩展训练数据，我们提出了一种针对角色动画的新型框架，使用扩散模型来保持细节特征的一致性，通过空间注意力合并细节特征，引入高效的姿势指导器来控制角色运动，并采用有效的时间建模方法来实现视频帧之间的流畅过渡，相比其他图像到视频方法，在角色动画领域取得了卓越的结果。此外，我们在时尚视频和人类舞蹈合成基准上评估了我们的方法，取得了最先进的结果。