  Reconstructing dynamic objects from monocular videos is a severely
underconstrained and challenging problem, and recent work has approached it in
various directions. However, owing to the ill-posed nature of this problem,
there has been no solution that can provide consistent, high-quality novel
views from camera positions that are significantly different from the training
views. In this work, we introduce Neural Parametric Gaussians (NPGs) to take on
this challenge by imposing a two-stage approach: first, we fit a low-rank
neural deformation model, which then is used as regularization for non-rigid
reconstruction in the second stage. The first stage learns the object's
deformations such that it preserves consistency in novel views. The second
stage obtains high reconstruction quality by optimizing 3D Gaussians that are
driven by the coarse model. To this end, we introduce a local 3D Gaussian
representation, where temporally shared Gaussians are anchored in and deformed
by local oriented volumes. The resulting combined model can be rendered as
radiance fields, resulting in high-quality photo-realistic reconstructions of
the non-rigidly deforming objects. We demonstrate that NPGs achieve superior
results compared to previous works, especially in challenging scenarios with
few multi-view cues.
