  Recently, flow-based frame interpolation methods have achieved great success
by first modeling optical flow between target and input frames, and then
building synthesis network for target frame generation. However, above cascaded
architecture can lead to large model size and inference delay, hindering them
from mobile and real-time applications. To solve this problem, we propose a
novel Progressive Motion Context Refine Network (PMCRNet) to predict motion
fields and image context jointly for higher efficiency. Different from others
that directly synthesize target frame from deep feature, we explore to simplify
frame interpolation task by borrowing existing texture from adjacent input
frames, which means that decoder in each pyramid level of our PMCRNet only
needs to update easier intermediate optical flow, occlusion merge mask and
image residual. Moreover, we introduce a new annealed multi-scale
reconstruction loss to better guide the learning process of this efficient
PMCRNet. Experiments on multiple benchmarks show that proposed approaches not
only achieve favorable quantitative and qualitative results but also reduces
current model size and running time significantly.
