@article{Yang2026, 
author = {Shuzhou Yang and Chong Mou and Jiwen Yu and Yuhan Wang and Xiandong Meng and Jian Zhang},
title = {Neural video field editing},
year = {2026},
journal = {Computational Visual Media},
volume = {12},
number = {5},
pages = {1339-1358},
keywords = {video editing, neural video fields (NVF), text-to-image (T2I) models},
url = {https://www.sciopen.com/article/10.26599/CVM.2025.9450500},
doi = {10.26599/CVM.2025.9450500},
abstract = {Diffusion models have revolutionized text-driven video editing. However, applying these methods to real-world editing encounters two significant challenges: (ⅰ) the rapid increase in GPU memory demand as the number of frames grows, and (ⅱ) inconsistency between frames in edited videos. To address these issues, we propose NVEdit, a novel text-driven video editing framework designed to mitigate memory overhead and improve editing consistency for long real-world videos. Specifically, we construct a neural video field, based on tri-plane and sparse grid representations, to enable the encoding of 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 used to preserve original temporal priors. Importantly, both the neural video field and T2I model are adaptable and replaceable, thus providing a basis for future research. Experiments demonstrate the ability of our approach to edit hundreds of frames with strong inter-frame consistency. Our project is available at https://nvedit.github.io/.}
}