论文标题
视频重演：在时间上学习时间变化的时间变化
Video-ReTime: Learning Temporally Varying Speediness for Time Remapping
论文作者
论文摘要
我们提出了一种生成时间重新映射的视频的方法，该视频匹配所需的目标持续时间，同时最大程度地保留自然视频动力学。我们的方法通过自学训练训练神经网络，以识别并准确地将视频播放速度变化的时间变化。要重新定时视频，我们1。使用模型来推断单个视频帧的缓慢，以及2。优化时间框架子采样以与模型的缓慢预测一致。我们证明，该模型可以更准确地检测播放速度变化，同时比以前的方法更有效。此外，我们提出了一种优化视频重新定义的优化，该优化可以对目标持续时间进行精确的控制，并且在更长的视频上比以前的方法更强大。我们通过转移到动作识别以及通过用户研究定性地对人工加速视频进行定量评估模型。
We propose a method for generating a temporally remapped video that matches the desired target duration while maximally preserving natural video dynamics. Our approach trains a neural network through self-supervision to recognize and accurately localize temporally varying changes in the video playback speed. To re-time videos, we 1. use the model to infer the slowness of individual video frames, and 2. optimize the temporal frame sub-sampling to be consistent with the model's slowness predictions. We demonstrate that this model can detect playback speed variations more accurately while also being orders of magnitude more efficient than prior approaches. Furthermore, we propose an optimization for video re-timing that enables precise control over the target duration and performs more robustly on longer videos than prior methods. We evaluate the model quantitatively on artificially speed-up videos, through transfer to action recognition, and qualitatively through user studies.
