This paper presents a simple, self-supervised method for magnifying subtle motions in video: given an input video and a magnification factor, we manipulate the video such that its new optical flow is scaled by the desired amount. To train our model, we propose a loss function that estimates the optical flow of the generated video and penalizes how far if deviates from the given magnification factor. Thus, training involves differentiating through a pretrained optical flow network. Since our model is self-supervised, we can further improve its performance through test-time adaptation, by finetuning it on the input video. It can also be easily extended to magnify the motions of only user-selected objects. Our approach avoids the need for synthetic magnification datasets that have been used to train prior learning-based approaches. Instead, it leverages the existing capabilities of off-the-shelf motion estimators. We demonstrate the effectiveness of our method through evaluations of both visual quality and quantitative metrics on a range of real-world and synthetic videos, and we show our method works for both supervised and unsupervised optical flow methods.

翻译：本文提出了一种简单、自监督的视频细微运动放大方法：给定输入视频和放大倍数，我们对视频进行处理，使其新光流按所需倍数缩放。为训练模型，我们提出一种损失函数，该函数估计生成视频的光流，并惩罚其偏离给定放大倍数的程度。因此，训练过程涉及通过预训练光流网络进行微分。由于模型是自监督的，我们可通过测试时自适应（即在输入视频上微调模型）进一步提升性能。该方法还可轻松扩展至仅放大用户选定物体的运动。本方法无需使用先前基于学习的方法所需合成的运动放大数据集，而是利用现成运动估计器的现有能力。通过在真实视频与合成视频上对视觉质量和定量指标的评估，我们验证了方法的有效性，并展示了该方法既适用于监督光流方法也适用于无监督光流方法。