  In this paper, we address the problem of building dense correspondences
between human images under arbitrary camera viewpoints and body poses. Prior
art either assumes small motion between frames or relies on local descriptors,
which cannot handle large motion or visually ambiguous body parts, e.g., left
vs. right hand. In contrast, we propose a deep learning framework that maps
each pixel to a feature space, where the feature distances reflect the geodesic
distances among pixels as if they were projected onto the surface of a 3D human
scan. To this end, we introduce novel loss functions to push features apart
according to their geodesic distances on the surface. Without any semantic
annotation, the proposed embeddings automatically learn to differentiate
visually similar parts and align different subjects into an unified feature
space. Extensive experiments show that the learned embeddings can produce
accurate correspondences between images with remarkable generalization
capabilities on both intra and inter subjects.
