  We present a solution for the goal of extracting a video from a single motion
blurred image to sequentially reconstruct the clear views of a scene as beheld
by the camera during the time of exposure. We first learn motion representation
from sharp videos in an unsupervised manner through training of a convolutional
recurrent video autoencoder network that performs a surrogate task of video
reconstruction. Once trained, it is employed for guided training of a motion
encoder for blurred images. This network extracts embedded motion information
from the blurred image to generate a sharp video in conjunction with the
trained recurrent video decoder. As an intermediate step, we also design an
efficient architecture that enables real-time single image deblurring and
outperforms competing methods across all factors: accuracy, speed, and
compactness. Experiments on real scenes and standard datasets demonstrate the
superiority of our framework over the state-of-the-art and its ability to
generate a plausible sequence of temporally consistent sharp frames.
