  This work presents a supervised learning based approach to the computer
vision problem of frame interpolation. The presented technique could also be
used in the cartoon animations since drawing each individual frame consumes a
noticeable amount of time. The most existing solutions to this problem use
unsupervised methods and focus only on real life videos with already high frame
rate. However, the experiments show that such methods do not work as well when
the frame rate becomes low and object displacements between frames becomes
large. This is due to the fact that interpolation of the large displacement
motion requires knowledge of the motion structure thus the simple techniques
such as frame averaging start to fail. In this work the deep convolutional
neural network is used to solve the frame interpolation problem. In addition,
it is shown that incorporating the prior information such as optical flow
improves the interpolation quality significantly.
