  We present an approach to infer the 3D shape, texture, and camera pose for an
object from a single RGB image, using only category-level image collections
with foreground masks as supervision. We represent the shape as an
image-conditioned implicit function that transforms the surface of a sphere to
that of the predicted mesh, while additionally predicting the corresponding
texture. To derive supervisory signal for learning, we enforce that: a) our
predictions when rendered should explain the available image evidence, and b)
the inferred 3D structure should be geometrically consistent with learned pixel
to surface mappings. We empirically show that our approach improves over prior
work that leverages similar supervision, and in fact performs competitively to
methods that use stronger supervision. Finally, as our method enables learning
with limited supervision, we qualitatively demonstrate its applicability over a
set of about 30 object categories.
