Large-scale text-to-video (T2V) diffusion models have great progress in
recent years in terms of visual quality, motion and temporal consistency.
However, the generation process is still a black box, where all attributes
(e.g., appearance, motion) are learned and generated jointly without precise
control ability other than rough text descriptions. Inspired by image animation
which decouples the video as one specific appearance with the corresponding
motion, we propose AnimateZero to unveil the pre-trained text-to-video
diffusion model, i.e., AnimateDiff, and provide more precise appearance and
motion control abilities for it. For appearance control, we borrow intermediate
latents and their features from the text-to-image (T2I) generation for ensuring
the generated first frame is equal to the given generated image. For temporal
control, we replace the global temporal attention of the original T2V model
with our proposed positional-corrected window attention to ensure other frames
align with the first frame well. Empowered by the proposed methods, AnimateZero
can successfully control the generating progress without further training. As a
zero-shot image animator for given images, AnimateZero also enables multiple
new applications, including interactive video generation and real image
animation. The detailed experiments demonstrate the effectiveness of the
proposed method in both T2V and related applications.

通过解耦视频为特定外观与相应动作的图像动画，我们提出了 AnimateZero 来揭示预训练的文本到视频扩散模型 AnimateDiff，并为其提供更精确的外观和动作控制能力。Anima teZero 无需进一步训练即可成功控制生成进程，对于给定图像，还可以实现多种新应用，包括交互式视频生成和真实图像动画。详细实验表明，该方法在 T2V 和相关应用中具有有效性。