Our method outperforms existing baselines, demonstrating a significant improvement in temporal consistency while preserving the motion of the original video.
Although our method excels at maintaining structural integrity, it struggles with edits needing structural changes, leading to visual artifacts when the image technique fails.
The internal representation of natural videos in the diffusion models reveals temporal redundancies, providing new avenues for enhancing video synthesis and text-to-video models.
A potential improvement lies in integrating our framework with enhanced decoders to mitigate high-frequency flickering, alongside the use of effective post-process deflickering techniques.
#text-driven-video-editing #image-diffusion-model #temporal-consistency #video-synthesis #video-editing-techniques
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