Abstract
We present LBS-AE; a self-supervised autoencoding algorithm for fitting articulated mesh models to point clouds.
As input, we take a sequence of point clouds to be registered as well as an artist-rigged mesh, i.e. a template mesh
equipped with a linear-blend skinning (LBS) deformation
space parameterized by a skeleton hierarchy. As output,
we learn an LBS-based autoencoder that produces registered meshes from the input point clouds. To bridge the gap
between the artist-defined geometry and the captured point
clouds, our autoencoder models pose-dependent deviations
from the template geometry. During training, instead of using explicit correspondences, such as key points or pose supervision, our method leverages LBS deformations to bootstrap the learning process. To avoid poor local minima
from erroneous point-to-point correspondences, we utilize a
structured Chamfer distance based on part-segmentations,
which are learned concurrently using self-supervision. We
demonstrate qualitative results on real captured hands, and
report quantitative evaluations on the FAUST benchmark
for body registration. Our method achieves performance
that is superior to other unsupervised approaches and comparable to methods using supervised examples.