  Recent success in casting Non-rigid Structure from Motion (NRSfM) as an
unsupervised deep learning problem has raised fundamental questions about what
novelty in NRSfM prior could the deep learning offer. In this paper we advocate
for a 3D deep auto-encoder framework to be used explicitly as the NRSfM prior.
The framework is unique as: (i) it learns the 3D auto-encoder weights solely
from 2D projected measurements, and (ii) it is Procrustean in that it jointly
resolves the unknown rigid pose for each shape instance. We refer to this
architecture as a Procustean Autoencoder for Unsupervised Lifting (PAUL), and
demonstrate state-of-the-art performance across a number of benchmarks in
comparison to recent innovations such as Deep NRSfM and C3PDO.
