  With the development of VR-related techniques, viewers can enjoy a realistic
and immersive experience through a head-mounted display, while omnidirectional
video with a low frame rate can lead to user dizziness. However, the prevailing
plane frame interpolation methodologies are unsuitable for Omnidirectional
Video Interpolation, chiefly due to the lack of models tailored to such videos
with strong distortion, compounded by the scarcity of valuable datasets for
Omnidirectional Video Frame Interpolation. In this paper, we introduce the
benchmark dataset, 360VFI, for Omnidirectional Video Frame Interpolation. We
present a practical implementation that introduces a distortion prior from
omnidirectional video into the network to modulate distortions. We especially
propose a pyramid distortion-sensitive feature extractor that uses the unique
characteristics of equirectangular projection (ERP) format as prior
information. Moreover, we devise a decoder that uses an affine transformation
to facilitate the synthesis of intermediate frames further. 360VFI is the first
dataset and benchmark that explores the challenge of Omnidirectional Video
Frame Interpolation. Through our benchmark analysis, we presented four
different distortion conditions scenes in the proposed 360VFI dataset to
evaluate the challenge triggered by distortion during interpolation. Besides,
experimental results demonstrate that Omnidirectional Video Interpolation can
be effectively improved by modeling for omnidirectional distortion.
