  In this paper, we propose a novel learning-based polygonal point set tracking
method. Compared to existing video object segmentation~(VOS) methods that
propagate pixel-wise object mask information, we propagate a polygonal point
set over frames.
  Specifically, the set is defined as a subset of points in the target contour,
and our goal is to track corresponding points on the target contour. Those
outputs enable us to apply various visual effects such as motion tracking, part
deformation, and texture mapping. To this end, we propose a new method to track
the corresponding points between frames by the global-local alignment with
delicately designed losses and regularization terms. We also introduce a novel
learning strategy using synthetic and VOS datasets that makes it possible to
tackle the problem without developing the point correspondence dataset. Since
the existing datasets are not suitable to validate our method, we build a new
polygonal point set tracking dataset and demonstrate the superior performance
of our method over the baselines and existing contour-based VOS methods. In
addition, we present visual-effects applications of our method on part
distortion and text mapping.
