  In this paper, we firstly present a dataset (X4K1000FPS) of 4K videos of 1000
fps with the extreme motion to the research community for video frame
interpolation (VFI), and propose an extreme VFI network, called XVFI-Net, that
first handles the VFI for 4K videos with large motion. The XVFI-Net is based on
a recursive multi-scale shared structure that consists of two cascaded modules
for bidirectional optical flow learning between two input frames (BiOF-I) and
for bidirectional optical flow learning from target to input frames (BiOF-T).
The optical flows are stably approximated by a complementary flow reversal
(CFR) proposed in BiOF-T module. During inference, the BiOF-I module can start
at any scale of input while the BiOF-T module only operates at the original
input scale so that the inference can be accelerated while maintaining highly
accurate VFI performance. Extensive experimental results show that our XVFI-Net
can successfully capture the essential information of objects with extremely
large motions and complex textures while the state-of-the-art methods exhibit
poor performance. Furthermore, our XVFI-Net framework also performs comparably
on the previous lower resolution benchmark dataset, which shows a robustness of
our algorithm as well. All source codes, pre-trained models, and proposed
X4K1000FPS datasets are publicly available at
https://github.com/JihyongOh/XVFI.
