# Channel Attention Is All You Need for Video Frame Interpolation

**Authors:** Myungsub Choi, Heewon Kim, Bohyung Han, Ning Xu, Kyoung Mu Lee  
**Published in:** Proceedings of the AAAI Conference on Artificial Intelligence  
**DOI:** [10.1609/aaai.v34i07.6693](https://doi.org/10.1609/aaai.v34i07.6693)  
**Date:** 2020-04-03  
**Summary page:** https://www.summarizepaper.com/en/paper/doi_10.1609--aaai.v34i07.6693/

## Abstract

Prevailing video frame interpolation techniques rely heavily on optical flow estimation and require additional model complexity and computational cost; it is also susceptible to error propagation in challenging scenarios with large motion and heavy occlusion. To alleviate the limitation, we propose a simple but effective deep neural network for video frame interpolation, which is end-to-end trainable and is free from a motion estimation network component. Our algorithm employs a special feature reshaping operation, referred to as PixelShuffle, with a channel attention, which replaces the optical flow computation module. The main idea behind the design is to distribute the information in a feature map into multiple channels and extract motion information by attending the channels for pixel-level frame synthesis. The model given by this principle turns out to be effective in the presence of challenging motion and occlusion. We construct a comprehensive evaluation benchmark and demonstrate that the proposed approach achieves outstanding performance compared to the existing models with a component for optical flow computation.