  In this paper, we present an approach for combining non-rigid
structure-from-motion (NRSfM) with deep generative models,and propose an
efficient framework for discovering trajectories in the latent space of 2D GANs
corresponding to changes in 3D geometry. Our approach uses recent advances in
NRSfM and enables editing of the camera and non-rigid shape information
associated with the latent codes without needing to retrain the generator. This
formulation provides an implicit dense 3D reconstruction as it enables the
image synthesis of novel shapes from arbitrary view angles and non-rigid
structure. The method is built upon a sparse backbone, where a neural regressor
is first trained to regress parameters describing the cameras and sparse
non-rigid structure directly from the latent codes. The latent trajectories
associated with changes in the camera and structure parameters are then
identified by estimating the local inverse of the regressor in the neighborhood
of a given latent code. The experiments show that our approach provides a
versatile, systematic way to model, analyze, and edit the geometry and
non-rigid structures of faces.
