  Parametric 3D models have enabled a wide variety of tasks in computer
graphics and vision, such as modeling human bodies, faces, and hands. However,
the construction of these parametric models is often tedious, as it requires
heavy manual tweaking, and they struggle to represent additional complexity and
details such as wrinkles or clothing. To this end, we propose Neural Parametric
Models (NPMs), a novel, learned alternative to traditional, parametric 3D
models, which does not require hand-crafted, object-specific constraints. In
particular, we learn to disentangle 4D dynamics into latent-space
representations of shape and pose, leveraging the flexibility of recent
developments in learned implicit functions. Crucially, once learned, our neural
parametric models of shape and pose enable optimization over the learned spaces
to fit to new observations, similar to the fitting of a traditional parametric
model, e.g., SMPL. This enables NPMs to achieve a significantly more accurate
and detailed representation of observed deformable sequences. We show that NPMs
improve notably over both parametric and non-parametric state of the art in
reconstruction and tracking of monocular depth sequences of clothed humans and
hands. Latent-space interpolation as well as shape / pose transfer experiments
further demonstrate the usefulness of NPMs.
