  We introduce a novel motion estimation method, MaskFlow, that is capable of
estimating accurate motion fields, even in very challenging cases with small
objects, large displacements and drastic appearance changes. In addition to
lower-level features, that are used in other Deep Neural Network (DNN)-based
motion estimation methods, MaskFlow draws from object-level features and
segmentations. These features and segmentations are used to approximate the
objects' translation motion field. We propose a novel and effective way of
incorporating the incomplete translation motion field into a subsequent motion
estimation network for refinement and completion. We also produced a new
challenging synthetic dataset with motion field ground truth, and also provide
extra ground truth for the object-instance matchings and corresponding
segmentation masks. We demonstrate that MaskFlow outperforms state of the art
methods when evaluated on our new challenging dataset, whilst still producing
comparable results on the popular FlyingThings3D benchmark dataset.
