Video frame interpolation (VFI) works generally predict intermediate frame(s)
by first estimating the motion between inputs and then warping the inputs to
the target time with the estimated motion. This approach, however, is not
optimal when the temporal distance between the input sequence increases as
existing motion estimation modules cannot effectively handle large motions.
Hence, VFI works perform well for small frame gaps and perform poorly as the
frame gap increases. In this work, we propose a novel framework to address this
problem. We argue that when there is a large gap between inputs, instead of
estimating imprecise motion that will eventually lead to inaccurate
interpolation, we can safely propagate from one side of the input up to a
reliable time frame using the other input as a reference. Then, the rest of the
intermediate frames can be interpolated using standard approaches as the
temporal gap is now narrowed. To this end, we propose a propagation network
(PNet) by extending the classic feature-level forecasting with a novel
motion-to-feature approach. To be thorough, we adopt a simple interpolation
model along with PNet as our full model and design a simple procedure to train
the full model in an end-to-end manner. Experimental results on several
benchmark datasets confirm the effectiveness of our method for long-term VFI
compared to state-of-the-art approaches.

本研究提出了一种基于传播网络（PNet）的新框架，旨在有效解决视频帧插值（VFI）中存在的大间隔问题，将一侧的输入传播到可靠时间帧，然后使用标准方法进行插值。此方法在多项基准测试数据集上的实验结果表明，在长期 VFI 方面，其比现有的最先进方法更加有效。