  Motion boundary detection is a crucial yet challenging problem. Prior methods
focus on analyzing the gradients and distributions of optical flow fields, or
use hand-crafted features for motion boundary learning. In this paper, we
propose the first dedicated end-to-end deep learning approach for motion
boundary detection, which we term as MoBoNet. We introduce a refinement network
structure which takes source input images, initial forward and backward optical
flows as well as corresponding warping errors as inputs and produces
high-resolution motion boundaries. Furthermore, we show that the obtained
motion boundaries, through a fusion sub-network we design, can in turn guide
the optical flows for removing the artifacts. The proposed MoBoNet is generic
and works with any optical flows. Our motion boundary detection and the refined
optical flow estimation achieve results superior to the state of the art.
