Low-Cost Test-Time Adaptation for Robust Video Editing
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arXiv
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| Autori principali: | , , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866915416940478464 |
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| author | Wang, Jianhui Chen, Yinda He, Yangfan Song, Xinyuan Xin, Yi Zhang, Dapeng Wan, Zhongwei Li, Bin Zhang, Rongchao |
| author_facet | Wang, Jianhui Chen, Yinda He, Yangfan Song, Xinyuan Xin, Yi Zhang, Dapeng Wan, Zhongwei Li, Bin Zhang, Rongchao |
| contents | Video editing is a critical component of content creation that transforms raw footage into coherent works aligned with specific visual and narrative objectives. Existing approaches face two major challenges: temporal inconsistencies due to failure in capturing complex motion patterns, and overfitting to simple prompts arising from limitations in UNet backbone architectures. While learning-based methods can enhance editing quality, they typically demand substantial computational resources and are constrained by the scarcity of high-quality annotated data. In this paper, we present Vid-TTA, a lightweight test-time adaptation framework that personalizes optimization for each test video during inference through self-supervised auxiliary tasks. Our approach incorporates a motion-aware frame reconstruction mechanism that identifies and preserves crucial movement regions, alongside a prompt perturbation and reconstruction strategy that strengthens model robustness to diverse textual descriptions. These innovations are orchestrated by a meta-learning driven dynamic loss balancing mechanism that adaptively adjusts the optimization process based on video characteristics. Extensive experiments demonstrate that Vid-TTA significantly improves video temporal consistency and mitigates prompt overfitting while maintaining low computational overhead, offering a plug-and-play performance boost for existing video editing models. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_21858 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Low-Cost Test-Time Adaptation for Robust Video Editing Wang, Jianhui Chen, Yinda He, Yangfan Song, Xinyuan Xin, Yi Zhang, Dapeng Wan, Zhongwei Li, Bin Zhang, Rongchao Computer Vision and Pattern Recognition Video editing is a critical component of content creation that transforms raw footage into coherent works aligned with specific visual and narrative objectives. Existing approaches face two major challenges: temporal inconsistencies due to failure in capturing complex motion patterns, and overfitting to simple prompts arising from limitations in UNet backbone architectures. While learning-based methods can enhance editing quality, they typically demand substantial computational resources and are constrained by the scarcity of high-quality annotated data. In this paper, we present Vid-TTA, a lightweight test-time adaptation framework that personalizes optimization for each test video during inference through self-supervised auxiliary tasks. Our approach incorporates a motion-aware frame reconstruction mechanism that identifies and preserves crucial movement regions, alongside a prompt perturbation and reconstruction strategy that strengthens model robustness to diverse textual descriptions. These innovations are orchestrated by a meta-learning driven dynamic loss balancing mechanism that adaptively adjusts the optimization process based on video characteristics. Extensive experiments demonstrate that Vid-TTA significantly improves video temporal consistency and mitigates prompt overfitting while maintaining low computational overhead, offering a plug-and-play performance boost for existing video editing models. |
| title | Low-Cost Test-Time Adaptation for Robust Video Editing |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2507.21858 |