Motion-Aware Video Frame Interpolation
Pengfei Han Fuhua Zhang Bin Zhao Xuelong Li
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.