  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.
