  This paper proposes a CNN cascade for semantic part segmentation guided by
pose-specific information encoded in terms of a set of landmarks (or
keypoints). There is large amount of prior work on each of these tasks
separately, yet, to the best of our knowledge, this is the first time in
literature that the interplay between pose estimation and semantic part
segmentation is investigated. To address this limitation of prior work, in this
paper, we propose a CNN cascade of tasks that firstly performs landmark
localisation and then uses this information as input for guiding semantic part
segmentation. We applied our architecture to the problem of facial part
segmentation and report large performance improvement over the standard
unguided network on the most challenging face datasets. Testing code and models
will be published online at http://cs.nott.ac.uk/~psxasj/.
