  Video frame interpolation aims to synthesize nonexistent frames in-between
the original frames. While significant advances have been made from the recent
deep convolutional neural networks, the quality of interpolation is often
reduced due to large object motion or occlusion. In this work, we propose a
video frame interpolation method which explicitly detects the occlusion by
exploring the depth information. Specifically, we develop a depth-aware flow
projection layer to synthesize intermediate flows that preferably sample closer
objects than farther ones. In addition, we learn hierarchical features to
gather contextual information from neighboring pixels. The proposed model then
warps the input frames, depth maps, and contextual features based on the
optical flow and local interpolation kernels for synthesizing the output frame.
Our model is compact, efficient, and fully differentiable. Quantitative and
qualitative results demonstrate that the proposed model performs favorably
against state-of-the-art frame interpolation methods on a wide variety of
datasets.
