Existing video frame interpolation (VFI) methods blindly predict where each
object is at a specific timestep t ("time indexing"), which struggles to
predict precise object movements. Given two images of a baseball, there are
infinitely many possible trajectories: accelerating or decelerating, straight
or curved. This often results in blurry frames as the method averages out these
possibilities. Instead of forcing the network to learn this complicated
time-to-location mapping implicitly together with predicting the frames, we
provide the network with an explicit hint on how far the object has traveled
between start and end frames, a novel approach termed "distance indexing". This
method offers a clearer learning goal for models, reducing the uncertainty tied
to object speeds. We further observed that, even with this extra guidance,
objects can still be blurry especially when they are equally far from both
input frames (i.e., halfway in-between), due to the directional ambiguity in
long-range motion. To solve this, we propose an iterative reference-based
estimation strategy that breaks down a long-range prediction into several
short-range steps. When integrating our plug-and-play strategies into
state-of-the-art learning-based models, they exhibit markedly sharper outputs
and superior perceptual quality in arbitrary time interpolations, using a
uniform distance indexing map in the same format as time indexing.
Additionally, distance indexing can be specified pixel-wise, which enables
temporal manipulation of each object independently, offering a novel tool for
video editing tasks like re-timing.

现有的视频帧插值方法通过时间索引盲目预测每个对象在特定时刻的位置，但很难预测对象的精确运动；为了解决这个问题，本文提出了一种名为 “距离索引” 的新方法，通过提供对象在起始和结束帧之间行进的距离作为网络的明确提示，将复杂的时间到位置映射问题与帧预测分离开来，同时还使用了基于参考的估计策略和迭代步骤的方法来解决长程运动中的模糊问题；而且，距离索引可以以像素为单位指定，可以独立对每个对象进行时间操作，为视频编辑任务如变速提供了一种新工具。