  With the prosperity of digital video industry, video frame interpolation has
arisen continuous attention in computer vision community and become a new
upsurge in industry. Many learning-based methods have been proposed and
achieved progressive results. Among them, a recent algorithm named quadratic
video interpolation (QVI) achieves appealing performance. It exploits
higher-order motion information (e.g. acceleration) and successfully models the
estimation of interpolated flow. However, its produced intermediate frames
still contain some unsatisfactory ghosting, artifacts and inaccurate motion,
especially when large and complex motion occurs. In this work, we further
improve the performance of QVI from three facets and propose an enhanced
quadratic video interpolation (EQVI) model. In particular, we adopt a rectified
quadratic flow prediction (RQFP) formulation with least squares method to
estimate the motion more accurately. Complementary with image pixel-level
blending, we introduce a residual contextual synthesis network (RCSN) to employ
contextual information in high-dimensional feature space, which could help the
model handle more complicated scenes and motion patterns. Moreover, to further
boost the performance, we devise a novel multi-scale fusion network (MS-Fusion)
which can be regarded as a learnable augmentation process. The proposed EQVI
model won the first place in the AIM2020 Video Temporal Super-Resolution
Challenge.
