  In this work, we focus on the task of learning and representing dense
correspondences in deformable object categories. While this problem has been
considered before, solutions so far have been rather ad-hoc for specific object
types (i.e., humans), often with significant manual work involved. However,
scaling the geometry understanding to all objects in nature requires more
automated approaches that can also express correspondences between related, but
geometrically different objects. To this end, we propose a new, learnable
image-based representation of dense correspondences. Our model predicts, for
each pixel in a 2D image, an embedding vector of the corresponding vertex in
the object mesh, therefore establishing dense correspondences between image
pixels and 3D object geometry. We demonstrate that the proposed approach
performs on par or better than the state-of-the-art methods for dense pose
estimation for humans, while being conceptually simpler. We also collect a new
in-the-wild dataset of dense correspondences for animal classes and demonstrate
that our framework scales naturally to the new deformable object categories.
