Video frame interpolation methodologies endeavor to create novel frames
betwixt extant ones, with the intent of augmenting the video's frame frequency.
However, current methods are prone to image blurring and spurious artifacts in
challenging scenarios involving occlusions and discontinuous motion. Moreover,
they typically rely on optical flow estimation, which adds complexity to
modeling and computational costs. To address these issues, we introduce a
Motion-Aware Video Frame Interpolation (MA-VFI) network, which directly
estimates intermediate optical flow from consecutive frames by introducing a
novel hierarchical pyramid module. It not only extracts global semantic
relationships and spatial details from input frames with different receptive
fields, enabling the model to capture intricate motion patterns, but also
effectively reduces the required computational cost and complexity.
Subsequently, a cross-scale motion structure is presented to estimate and
refine intermediate flow maps by the extracted features. This approach
facilitates the interplay between input frame features and flow maps during the
frame interpolation process and markedly heightens the precision of the
intervening flow delineations. Finally, a discerningly fashioned loss centered
around an intermediate flow is meticulously contrived, serving as a deft rudder
to skillfully guide the prognostication of said intermediate flow, thereby
substantially refining the precision of the intervening flow mappings.
Experiments illustrate that MA-VFI surpasses several representative VFI methods
across various datasets, and can enhance efficiency while maintaining
commendable efficacy.

该研究介绍了一种 Motion-Aware Video Frame Interpolation (MA-VFI) 网络，通过引入新颖的分层金字塔模块，直接估计相邻帧之间的中间光流，从而解决了现有方法在复杂场景（包括遮挡和不连续运动）中易产生图像模糊和伪影的问题，并有效降低了计算成本和复杂性。实验证明，该方法在多个数据集上超越了几种代表性的视频帧插值方法，既提高了效率又保持了良好的准确性。