HyperNVD: Accelerating Neural Video Decomposition via Hypernetworks
Maria Pilligua, Danna Xue, Javier Vazquez-Corral
Abstract
Decomposing a video into a layer-based representation is crucial for easy video editing for the creative industries, as it enables independent editing of specific layers. Existing video-layer decomposition models rely on implicit neural representations (INRs) trained independently for each video, making the process time-consuming when applied to new videos. Noticing this limitation, we propose a meta-learning strategy to learn a generic video decomposition model to speed up the training on new videos. Our model is based on a hypernetwork architecture which, given a video-encoder embedding, generates the parameters for a compact INR-based neural video decomposition model. Our strategy mitigates the problem of single-video overfitting and, importantly, shortens the convergence of video decomposition on new, unseen videos. Our code is available at: https://hypernvd.github.io/
BibTeX
@InProceedings{Pilligua_2025_CVPR,
author = {Pilligua, Maria and Xue, Danna and Vazquez-Corral, Javier},
title = {HyperNVD: Accelerating Neural Video Decomposition via Hypernetworks},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {22933-22942}
}