  We study the challenging problem of recovering detailed motion from a single
motion-blurred image. Existing solutions to this problem estimate a single
image sequence without considering the motion ambiguity for each region.
Therefore, the results tend to converge to the mean of the multi-modal
possibilities. In this paper, we explicitly account for such motion ambiguity,
allowing us to generate multiple plausible solutions all in sharp detail. The
key idea is to introduce a motion guidance representation, which is a compact
quantization of 2D optical flow with only four discrete motion directions.
Conditioned on the motion guidance, the blur decomposition is led to a
specific, unambiguous solution by using a novel two-stage decomposition
network. We propose a unified framework for blur decomposition, which supports
various interfaces for generating our motion guidance, including human input,
motion information from adjacent video frames, and learning from a video
dataset. Extensive experiments on synthesized datasets and real-world data show
that the proposed framework is qualitatively and quantitatively superior to
previous methods, and also offers the merit of producing physically plausible
and diverse solutions. Code is available at
https://github.com/zzh-tech/Animation-from-Blur.
