AnimateZero: Video Diffusion Models are Zero-Shot Image Animators

TL;DR

AnimateZero leverages pre-trained text-to-video diffusion models to enable precise, zero-shot control over image appearance and motion, facilitating interactive video generation and real image animation without additional training.

Contribution

The paper introduces AnimateZero, a method that decouples appearance and motion control in pre-trained T2V diffusion models, achieving zero-shot image animation and enhanced controllability.

Findings

Effective appearance control using intermediate latents from T2I models.

Temporal consistency improved with positional-corrected window attention.

Successful zero-shot animation of images and new applications demonstrated.

Abstract

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.