HyperNVD: Accelerating Neural Video Decomposition via Hypernetworks
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arXiv
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866913750514139136 |
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| author | Pilligua, Maria Xue, Danna Vazquez-Corral, Javier |
| author_facet | Pilligua, Maria Xue, Danna Vazquez-Corral, Javier |
| contents | 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/ |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_17276 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | HyperNVD: Accelerating Neural Video Decomposition via Hypernetworks Pilligua, Maria Xue, Danna Vazquez-Corral, Javier Computer Vision and Pattern Recognition 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/ |
| title | HyperNVD: Accelerating Neural Video Decomposition via Hypernetworks |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2503.17276 |