  Existing learning-based frame interpolation algorithms extract consecutive
frames from high-speed natural videos to train the model. Compared to natural
videos, cartoon videos are usually in a low frame rate. Besides, the motion
between consecutive cartoon frames is typically nonlinear, which breaks the
linear motion assumption of interpolation algorithms. Thus, it is unsuitable
for generating a training set directly from cartoon videos. For better adapting
frame interpolation algorithms from nature video to animation video, we present
AutoFI, a simple and effective method to automatically render training data for
deep animation video interpolation. AutoFI takes a layered architecture to
render synthetic data, which ensures the assumption of linear motion.
Experimental results show that AutoFI performs favorably in training both DAIN
and ANIN. However, most frame interpolation algorithms will still fail in
error-prone areas, such as fast motion or large occlusion. Besides AutoFI, we
also propose a plug-and-play sketch-based post-processing module, named SktFI,
to refine the final results using user-provided sketches manually. With AutoFI
and SktFI, the interpolated animation frames show high perceptual quality.
