  We present a technique for synthesizing a motion blurred image from a pair of
unblurred images captured in succession. To build this system we motivate and
design a differentiable "line prediction" layer to be used as part of a neural
network architecture, with which we can learn a system to regress from image
pairs to motion blurred images that span the capture time of the input image
pair. Training this model requires an abundance of data, and so we design and
execute a strategy for using frame interpolation techniques to generate a
large-scale synthetic dataset of motion blurred images and their respective
inputs. We additionally capture a high quality test set of real motion blurred
images, synthesized from slow motion videos, with which we evaluate our model
against several baseline techniques that can be used to synthesize motion blur.
Our model produces higher accuracy output than our baselines, and is
significantly faster than baselines with competitive accuracy.
