Video stabilization is a longstanding computer vision problem, particularly pixel-level synthesis solutions for video stabilization which synthesize full frames add to the complexity of this task. These techniques aim to stabilize videos by synthesizing full frames while enhancing the stability of the considered video. This intensifies the complexity of the task due to the distinct mix of unique motion profiles and visual content present in each video sequence, making robust generalization with fixed parameters difficult. In our study, we introduce a novel approach to enhance the performance of pixel-level synthesis solutions for video stabilization by adapting these models to individual input video sequences. The proposed adaptation exploits low-level visual cues accessible during test-time to improve both the stability and quality of resulting videos. We highlight the efficacy of our methodology of "test-time adaptation" through simple fine-tuning of one of these models, followed by significant stability gain via the integration of meta-learning techniques. Notably, significant improvement is achieved with only a single adaptation step. The versatility of the proposed algorithm is demonstrated by consistently improving the performance of various pixel-level synthesis models for video stabilization in real-world scenarios.

翻译：视频稳定是一个长期存在的计算机视觉问题，特别是针对视频稳定的像素级合成解决方案，这些方案通过合成全帧来增加任务的复杂性。这些技术旨在通过合成全帧来稳定视频，同时增强视频的稳定性。由于每个视频序列中独特的运动特征和视觉内容的混合，这使得任务复杂性增加，导致使用固定参数进行鲁棒泛化变得困难。在我们的研究中，我们提出了一种新颖方法，通过将像素级合成模型适应于单个输入视频序列来提升视频稳定的性能。所提出的适应方法利用测试时可访问的低级视觉线索，改善生成视频的稳定性和质量。我们通过对其中一个模型进行简单微调，展示了“测试时适应”方法的效果，并通过集成元学习技术显著提升了稳定性。值得注意的是，仅通过一次适应步骤便能实现显著改进。所提算法的通用性通过在实际场景中持续提升多种视频稳定像素级合成模型的性能得到了验证。