  Neural shape models can represent complex 3D shapes with a compact latent
space. When applied to dynamically deforming shapes such as the human hands,
however, they would need to preserve temporal coherence of the deformation as
well as the intrinsic identity of the subject. These properties are difficult
to regularize with manually designed loss functions. In this paper, we learn a
neural deformation model that disentangles the identity-induced shape
variations from pose-dependent deformations using implicit neural functions. We
perform template-free unsupervised learning on 3D scans without explicit mesh
correspondence or semantic correspondences of shapes across subjects. We can
then apply the learned model to reconstruct partial dynamic 4D scans of novel
subjects performing unseen actions. We propose two methods to integrate global
pose alignment with our neural deformation model. Experiments demonstrate the
efficacy of our method in the disentanglement of identities and pose. Our
method also outperforms traditional skeleton-driven models in reconstructing
surface details such as palm prints or tendons without limitations from a fixed
template.
