Low-Cost Test-Time Adaptation for Robust Video Editing

• URL: http://arxiv.org/abs/2507.21858v1
• Date: Tue, 29 Jul 2025 14:31:17 GMT
• Title: Low-Cost Test-Time Adaptation for Robust Video Editing
• Authors: Jianhui Wang, Yinda Chen, Yangfan He, Xinyuan Song, Yi Xin, Dapeng Zhang, Zhongwei Wan, Bin Li, Rongchao Zhang,
• Abstract summary: Video editing is a critical component of content creation that transforms raw footage into coherent works aligned with specific visual and narrative objectives.<n>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.<n>We present Vid-TTA, a lightweight test-time adaptation framework that personalizes optimization for each test video during inference through self-supervised auxiliary tasks.
• Score: 4.707015344498921
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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