  We propose a weakly-supervised multi-view learning approach to learn
category-specific surface mapping without dense annotations. We learn the
underlying surface geometry of common categories, such as human faces, cars,
and airplanes, given instances from those categories. While traditional
approaches solve this problem using extensive supervision in the form of
pixel-level annotations, we take advantage of the fact that pixel-level UV and
mesh predictions can be combined with 3D reprojections to form consistency
cycles. As a result of exploiting these cycles, we can establish a dense
correspondence mapping between image pixels and the mesh acting as a
self-supervisory signal, which in turn helps improve our overall estimates. Our
approach leverages information from multiple views of the object to establish
additional consistency cycles, thus improving surface mapping understanding
without the need for explicit annotations. We also propose the use of
deformation fields for predictions of an instance specific mesh. Given the lack
of datasets providing multiple images of similar object instances from
different viewpoints, we generate and release a multi-view ShapeNet Cars and
Airplanes dataset created by rendering ShapeNet meshes using a 360 degree
camera trajectory around the mesh. For the human faces category, we process and
adapt an existing dataset to a multi-view setup. Through experimental
evaluations, we show that, at test time, our method can generate accurate
variations away from the mean shape, is multi-view consistent, and performs
comparably to fully supervised approaches.
