WAVE: Warping DDIM Inversion Features for Zero-shot Text-to-Video Editing

Text-driven video editing has emerged as a prominent application based on the breakthroughs of image diffusion models. Existing state-of-the-art methods focus on zero-shot frameworks due to limited training data and to limit computational expense. To preserve structure consistency, previous frameworks usually employ Denoising Diffusion Implicit Model (DDIM) inversion to provide inverted noise latents as guidance. The key challenge lies in limiting errors caused by the randomness and inaccuracy in each step of the in the naive DDIM inversion process, which can lead to temporal inconsistency in video editing tasks. Our observation indicates that incorporating temporal keyframe information can alleviate the accumulated error during inversion. In this paper, we propose an effective warping strategy in the feature domain to obtain high-quality DDIM inverted noise latents. Specifically, we shuffle the editing frames randomly in each timestep and use optical flow extracted from the source video to propagate the latent features of the first keyframe to subsequent keyframes. Moreover, we develop a comprehensive zero-shot framework that adapts to this strategy in both the inversion and denoising processes, thereby facilitating the generation of consistent edited videos.