# Learn to Model Motion from Blurry Footages

## TL;DR

This paper introduces a hybrid CNN and optical flow framework that effectively models motion from blurry footage by capturing blur features and jointly estimating deblurring and motion fields, trained end-to-end on synthetic data.

## Contribution

It presents a novel learnable directional filtering layer within a CNN integrated into an iterative optical flow framework for motion estimation from blurry videos.

## Key findings

- Achieves competitive accuracy against state-of-the-art methods.
- Effectively models both deblurring and motion estimation.
- End-to-end training on synthetic data enhances performance.

## Abstract

It is difficult to recover the motion field from a real-world footage given a mixture of camera shake and other photometric effects. In this paper we propose a hybrid framework by interleaving a Convolutional Neural Network (CNN) and a traditional optical flow energy. We first conduct a CNN architecture using a novel learnable directional filtering layer. Such layer encodes the angle and distance similarity matrix between blur and camera motion, which is able to enhance the blur features of the camera-shake footages. The proposed CNNs are then integrated into an iterative optical flow framework, which enable the capability of modelling and solving both the blind deconvolution and the optical flow estimation problems simultaneously. Our framework is trained end-to-end on a synthetic dataset and yields competitive precision and performance against the state-of-the-art approaches.