  Parametric 3D models have formed a fundamental role in modeling deformable
objects, such as human bodies, faces, and hands; however, the construction of
such parametric models requires significant manual intervention and domain
expertise. Recently, neural implicit 3D representations have shown great
expressibility in capturing 3D shape geometry. We observe that deformable
object motion is often semantically structured, and thus propose to learn
Structured-implicit PArametric Models (SPAMs) as a deformable object
representation that structurally decomposes non-rigid object motion into
part-based disentangled representations of shape and pose, with each being
represented by deep implicit functions. This enables a structured
characterization of object movement, with part decomposition characterizing a
lower-dimensional space in which we can establish coarse motion correspondence.
In particular, we can leverage the part decompositions at test time to fit to
new depth sequences of unobserved shapes, by establishing part correspondences
between the input observation and our learned part spaces; this guides a robust
joint optimization between the shape and pose of all parts, even under dramatic
motion sequences. Experiments demonstrate that our part-aware shape and pose
understanding lead to state-of-the-art performance in reconstruction and
tracking of depth sequences of complex deforming object motion. We plan to
release models to the public at https://pablopalafox.github.io/spams.
