  We propose Geometric Neural Parametric Models (GNPM), a learned parametric
model that takes into account the local structure of data to learn disentangled
shape and pose latent spaces of 4D dynamics, using a geometric-aware
architecture on point clouds. Temporally consistent 3D deformations are
estimated without the need for dense correspondences at training time, by
exploiting cycle consistency. Besides its ability to learn dense
correspondences, GNPMs also enable latent-space manipulations such as
interpolation and shape/pose transfer. We evaluate GNPMs on various datasets of
clothed humans, and show that it achieves comparable performance to
state-of-the-art methods that require dense correspondences during training.
