Deep Frame Interpolation

TL;DR

This paper introduces a supervised deep learning approach for frame interpolation that outperforms unsupervised methods, especially in scenarios with large object displacements, by leveraging optical flow information.

Contribution

The paper presents a novel supervised deep convolutional neural network for frame interpolation that incorporates optical flow to improve performance on low frame rate videos.

Findings

Supervised deep learning improves frame interpolation quality.

Incorporating optical flow significantly enhances results.

Method performs well on animations and videos with large displacements.

Abstract

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.