Large motion poses a critical challenge in Video Frame Interpolation (VFI)
task. Existing methods are often constrained by limited receptive fields,
resulting in sub-optimal performance when handling scenarios with large motion.
In this paper, we introduce a new pipeline for VFI, which can effectively
integrate global-level information to alleviate issues associated with large
motion. Specifically, we first estimate a pair of initial intermediate flows
using a high-resolution feature map for extracting local details. Then, we
incorporate a sparse global matching branch to compensate for flow estimation,
which consists of identifying flaws in initial flows and generating sparse flow
compensation with a global receptive field. Finally, we adaptively merge the
initial flow estimation with global flow compensation, yielding a more accurate
intermediate flow. To evaluate the effectiveness of our method in handling
large motion, we carefully curate a more challenging subset from commonly used
benchmarks. Our method demonstrates the state-of-the-art performance on these
VFI subsets with large motion.

通过引入一个新的管道，本文提出了一种有效地整合全局级信息以缓解大运动问题的视频帧插值方法，在处理大运动场景时展现了最先进的性能。