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:this https URL
View on arXiv@article{pilligua2025_2503.17276,
title={ HyperNVD: Accelerating Neural Video Decomposition via Hypernetworks },
author={ Maria Pilligua and Danna Xue and Javier Vazquez-Corral },
journal={arXiv preprint arXiv:2503.17276},
year={ 2025 }
}