  Capitalizing on the rapid development of neural networks, recent video frame
interpolation (VFI) methods have achieved notable improvements. However, they
still fall short for real-world videos containing large motions. Complex
deformation and/or occlusion caused by large motions make it an extremely
difficult problem in video frame interpolation. In this paper, we propose a
simple yet effective solution, H-VFI, to deal with large motions in video frame
interpolation. H-VFI contributes a hierarchical video interpolation transformer
(HVIT) to learn a deformable kernel in a coarse-to-fine strategy in multiple
scales. The learnt deformable kernel is then utilized in convolving the input
frames for predicting the interpolated frame. Starting from the smallest scale,
H-VFI updates the deformable kernel by a residual in succession based on former
predicted kernels, intermediate interpolated results and hierarchical features
from transformer. Bias and masks to refine the final outputs are then predicted
by a transformer block based on interpolated results. The advantage of such a
progressive approximation is that the large motion frame interpolation problem
can be decomposed into several relatively simpler sub-tasks, which enables a
very accurate prediction in the final results. Another noteworthy contribution
of our paper consists of a large-scale high-quality dataset, YouTube200K, which
contains videos depicting a great variety of scenarios captured at high
resolution and high frame rate. Extensive experiments on multiple frame
interpolation benchmarks validate that H-VFI outperforms existing
state-of-the-art methods especially for videos with large motions.
