Large-scale text-to-image (T2I) diffusion models have been extended for
text-guided video editing, yielding impressive zero-shot video editing
performance. Nonetheless, the generated videos usually show spatial
irregularities and temporal inconsistencies as the temporal characteristics of
videos have not been faithfully modeled. In this paper, we propose an elegant
yet effective Temporal-Consistent Video Editing (TCVE) method, to mitigate the
temporal inconsistency challenge for robust text-guided video editing. In
addition to the utilization of a pretrained 2D Unet for spatial content
manipulation, we establish a dedicated temporal Unet architecture to faithfully
capture the temporal coherence of the input video sequences. Furthermore, to
establish coherence and interrelation between the spatial-focused and
temporal-focused components, a cohesive joint spatial-temporal modeling unit is
formulated. This unit effectively interconnects the temporal Unet with the
pretrained 2D Unet, thereby enhancing the temporal consistency of the generated
video output while simultaneously preserving the capacity for video content
manipulation. Quantitative experimental results and visualization results
demonstrate that TCVE achieves state-of-the-art performance in both video
temporal consistency and video editing capability, surpassing existing
benchmarks in the field.

通过建立一个优雅而高效的 TCVE（Temporal-Consistent Video Editing）方法，本研究在大规模文本到图像（T2I）扩散模型的基础上，解决了文本引导视频编辑中的时间不连贯问题，取得了视频时序一致性和编辑能力方面的最新突破，并超越了该领域的现有基准。