  Traditional unsupervised optical flow methods are vulnerable to occlusions
and motion boundaries due to lack of object-level information. Therefore, we
propose UnSAMFlow, an unsupervised flow network that also leverages object
information from the latest foundation model Segment Anything Model (SAM). We
first include a self-supervised semantic augmentation module tailored to SAM
masks. We also analyze the poor gradient landscapes of traditional smoothness
losses and propose a new smoothness definition based on homography instead. A
simple yet effective mask feature module has also been added to further
aggregate features on the object level. With all these adaptations, our method
produces clear optical flow estimation with sharp boundaries around objects,
which outperforms state-of-the-art methods on both KITTI and Sintel datasets.
Our method also generalizes well across domains and runs very efficiently.
