  This work proposes novel hyperparameter-free losses for single view 3D
reconstruction with morphable models (3DMM). We dispense with the
hyperparameters used in other works by exploiting geometry, so that the shape
of the object and the camera pose are jointly optimized in a sole term
expression. This simplification reduces the optimization time and its
complexity. Moreover, we propose a novel implicit regularization technique
based on random virtual projections that does not require additional 2D or 3D
annotations. Our experiments suggest that minimizing a shape reprojection error
together with the proposed implicit regularization is especially suitable for
applications that require precise alignment between geometry and image spaces,
such as augmented reality. We evaluate our losses on a large scale dataset with
3D ground truth and publish our implementations to facilitate reproducibility
and public benchmarking in this field.
