  Diffusion models have revolutionized text-driven video editing. However,
applying these methods to real-world editing encounters two significant
challenges: (1) the rapid increase in GPU memory demand as the number of frames
grows, and (2) the inter-frame inconsistency in edited videos. To this end, we
propose NVEdit, a novel text-driven video editing framework designed to
mitigate memory overhead and improve consistent editing for real-world long
videos. Specifically, we construct a neural video field, powered by tri-plane
and sparse grid, to enable encoding long videos with hundreds of frames in a
memory-efficient manner. Next, we update the video field through off-the-shelf
Text-to-Image (T2I) models to impart text-driven editing effects. A progressive
optimization strategy is developed to preserve original temporal priors.
Importantly, both the neural video field and T2I model are adaptable and
replaceable, thus inspiring future research. Experiments demonstrate the
ability of our approach to edit hundreds of frames with impressive inter-frame
consistency. Our project is available at: https://nvedit.github.io/.
