  This paper presents a new deformable convolution-based video frame
interpolation (VFI) method, using a coarse to fine 3D CNN to enhance the
multi-flow prediction. This model first extracts spatio-temporal features at
multiple scales using a 3D CNN, and estimates multi-flows using these features
in a coarse-to-fine manner. The estimated multi-flows are then used to warp the
original input frames as well as context maps, and the warped results are fused
by a synthesis network to produce the final output. This VFI approach has been
fully evaluated against 12 state-of-the-art VFI methods on three commonly used
test databases. The results evidently show the effectiveness of the proposed
method, which offers superior interpolation performance over other state of the
art algorithms, with PSNR gains up to 0.19dB.
