Video stabilization refers to the problem of transforming a shaky video into a visually pleasing one. The question of how to strike a good trade-off between visual quality and computational speed has remained one of the open challenges in video stabilization. Inspired by the analogy between wobbly frames and jigsaw puzzles, we propose an iterative optimization-based learning approach using synthetic datasets for video stabilization, which consists of two interacting submodules: motion trajectory smoothing and full-frame outpainting. First, we develop a two-level (coarse-to-fine) stabilizing algorithm based on the probabilistic flow field. The confidence map associated with the estimated optical flow is exploited to guide the search for shared regions through backpropagation. Second, we take a divide-and-conquer approach and propose a novel multiframe fusion strategy to render full-frame stabilized views. An important new insight brought about by our iterative optimization approach is that the target video can be interpreted as the fixed point of nonlinear mapping for video stabilization. We formulate video stabilization as a problem of minimizing the amount of jerkiness in motion trajectories, which guarantees convergence with the help of fixed-point theory. Extensive experimental results are reported to demonstrate the superiority of the proposed approach in terms of computational speed and visual quality. The code will be available on GitHub.

翻译：视频稳定指的是将抖动视频转换为视觉上悦目视频的问题。如何在视觉质量与计算速度之间取得良好平衡，一直是视频稳定领域尚未解决的挑战之一。受抖动帧与拼图游戏之间相似性的启发，我们提出了一种基于迭代优化的学习方法，利用合成数据集进行视频稳定，该方法包含两个相互作用的子模块：运动轨迹平滑和全帧外推填充。首先，我们开发了一种基于概率流场的两级（从粗到细）稳定算法。利用与估计光流相关联的置信度图，通过反向传播指导共享区域的搜索。其次，我们采取分而治之的策略，提出了一种新颖的多帧融合策略来渲染全帧稳定视图。我们的迭代优化方法带来的一个重要新见解是，目标视频可以被解释为视频稳定非线性映射的不动点。我们将视频稳定表述为最小化运动轨迹抖动量的优化问题，借助不动点理论保证了算法的收敛性。大量实验结果表明，该方法在计算速度和视觉质量方面均具有优越性。代码将在GitHub上公开。