  We propose a general purpose approach to detect landmarks with improved
temporal consistency, and personalization. Most sparse landmark detection
methods rely on laborious, manually labelled landmarks, where inconsistency in
annotations over a temporal volume leads to sub-optimal landmark learning.
Further, high-quality landmarks with personalization is often hard to achieve.
We pose landmark detection as an image translation problem. We capture two sets
of unpaired marked (with paint) and unmarked videos. We then use a generative
adversarial network and cyclic consistency to predict deformations of landmark
templates that simulate markers on unmarked images until these images are
indistinguishable from ground-truth marked images. Our novel method does not
rely on manually labelled priors, is temporally consistent, and image class
agnostic -- face, and hand landmarks detection examples are shown.
