  The recovery of 3D shape and pose from 2D landmarks stemming from a large
ensemble of images can be viewed as a non-rigid structure from motion (NRSfM)
problem. Classical NRSfM approaches, however, are problematic as they rely on
heuristic priors on the 3D structure (e.g. low rank) that do not scale well to
large datasets. Learning-based methods are showing the potential to reconstruct
a much broader set of 3D structures than classical methods -- dramatically
expanding the importance of NRSfM to atemporal unsupervised 2D to 3D lifting.
Hitherto, these learning approaches have not been able to effectively model
perspective cameras or handle missing/occluded points -- limiting their
applicability to in-the-wild datasets. In this paper, we present a generalized
strategy for improving learning-based NRSfM methods to tackle the above issues.
Our approach, Deep NRSfM++, achieves state-of-the-art performance across
numerous large-scale benchmarks, outperforming both classical and
learning-based 2D-3D lifting methods.
