面向鲁棒视频编辑的低成本测试时自适应
Low-Cost Test-Time Adaptation for Robust Video Editing
AI总结：
针对现有视频编辑方法时序不一致、提示过拟合及计算成本高的问题，提出轻量级测试时自适应框架Vid-TTA，通过运动感知重建、提示扰动及元学习动态平衡机制，以低开销提升编辑模型的时序一致性与鲁棒性。
摘要：
视频编辑是内容创作的核心环节，可将原始素材转化为符合特定视觉与叙事目标的连贯作品。现有方法面临两大挑战：一是因无法捕捉复杂运动模式导致的时序不一致，二是UNet骨干架构的局限性引发的对简单提示的过拟合。尽管基于学习的方法能提升编辑质量，但它们通常需要大量计算资源，且受限于高质量标注数据的稀缺。本文提出Vid-TTA，这是一种轻量级测试时自适应框架，可在推理阶段通过自监督辅助任务为每个测试视频定制优化。该方法包含运动感知帧重建机制，可识别并保留关键运动区域，还配备提示扰动与重建策略，以增强模型对多样文本描述的鲁棒性。这些创新由元学习驱动的动态损失平衡机制统筹，该机制可根据视频特征自适应调整优化过程。大量实验表明，Vid-TTA在保持低计算开销的同时，显著提升了视频时序一致性并缓解了提示过拟合问题，可为现有视频编辑模型提供即插即用的性能提升。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
