  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.
