  Motion-based video frame interpolation (VFI) methods have made remarkable
progress with the development of deep convolutional networks over the past
years. While their performance is often jeopardized by the inaccuracy of flow
map estimation, especially in the case of large motion and occlusion. In this
paper, we propose a flow guidance deformable compensation network (FGDCN) to
overcome the drawbacks of existing motion-based methods. FGDCN decomposes the
frame sampling process into two steps: a flow step and a deformation step.
Specifically, the flow step utilizes a coarse-to-fine flow estimation network
to directly estimate the intermediate flows and synthesizes an anchor frame
simultaneously. To ensure the accuracy of the estimated flow, a distillation
loss and a task-oriented loss are jointly employed in this step. Under the
guidance of the flow priors learned in step one, the deformation step designs a
pyramid deformable compensation network to compensate for the missing details
of the flow step. In addition, a pyramid loss is proposed to supervise the
model in both the image and frequency domain. Experimental results show that
the proposed algorithm achieves excellent performance on various datasets with
fewer parameters.
