  This paper introduces an unsupervised loss for training parametric
deformation shape generators. The key idea is to enforce the preservation of
local rigidity among the generated shapes. Our approach builds on an
approximation of the as-rigid-as possible (or ARAP) deformation energy. We show
how to develop the unsupervised loss via a spectral decomposition of the
Hessian of the ARAP energy. Our loss nicely decouples pose and shape variations
through a robust norm. The loss admits simple closed-form expressions. It is
easy to train and can be plugged into any standard generation models, e.g.,
variational auto-encoder (VAE) and auto-decoder (AD). Experimental results show
that our approach outperforms existing shape generation approaches considerably
on public benchmark datasets of various shape categories such as human, animal
and bone.
