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arXiv · 2503.17276

HyperNVD: Accelerating Neural Video Decomposition via Hypernetworks

Maria Pilligua·Danna Xue·Javier Vazquez-Corral
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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/

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Maria Pilligua, Danna Xue, Javier Vazquez-Corral. 2025-03-21. HyperNVD: Accelerating Neural Video Decomposition via Hypernetworks. https://arxiv.org/abs/2503.17276

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