When observing the visual world, temporal phenomena are ubiquitous: people walk, cars drive, rivers flow, clouds drift, and shadows elongate. Some of these, like water splashing and cloud motion, occur over time intervals that are either too short or too long for humans to easily observe. High-speed and timelapse videos provide a popular and compelling way to visualize these phenomena, but many real-world scenes exhibit motions occurring at a variety of rates. Once a framerate is chosen, phenomena at other rates are at best invisible, and at worst create distracting artifacts. In this article, we propose to automatically normalize the pixel-space speed of different motions in an input video to produce a seamless output with spatiotemporally varying framerate. To achieve this, we propose to analyze scenes at different timescales to isolate and analyze motions that occur at vastly different rates. Our method optionally allows a user to specify additional constraints according to artistic preferences. The motion normalized output provides a novel way to compactly visualize the changes occurring in a scene over a broad range of timescales.

中文翻译：

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通过运动归一化进行场景汇总

观察视觉世界时，时空现象无处不在：人们走路，汽车行驶，河流流淌，云朵飘移，阴影拉长。其中一些事件（例如水溅和云运动）发生在时间间隔上，时间间隔太短或太长，人类无法轻易观察到。高速和延时视频提供了一种流行且引人注目的方式来可视化这些现象，但许多现实世界场景中的动作均以各种速率发生。一旦选择了帧速率，处于其他速率的现象充其量是不可见的，而在最坏的情况下会产生分散注意力的伪影。在本文中，我们建议自动归一化输入视频中不同运动的像素空间速度，以产生时空变化的帧速率的无缝输出。为此，我们建议分析不同场景的场景。时间尺度隔离和分析以非常不同的速率发生的运动。我们的方法可选地允许用户根据艺术喜好指定其他约束。运动归一化输出提供了一种新颖的方法，可以在广泛的时间范围内紧凑地可视化场景中发生的变化。