  Recent years have witnessed significant progress in the field of neural
surface reconstruction. While the extensive focus was put on volumetric and
implicit approaches, a number of works have shown that explicit graphics
primitives such as point clouds can significantly reduce computational
complexity, without sacrificing the reconstructed surface quality. However,
less emphasis has been put on modeling dynamic surfaces with point primitives.
In this work, we present a dynamic point field model that combines the
representational benefits of explicit point-based graphics with implicit
deformation networks to allow efficient modeling of non-rigid 3D surfaces.
Using explicit surface primitives also allows us to easily incorporate
well-established constraints such as-isometric-as-possible regularisation.
While learning this deformation model is prone to local optima when trained in
a fully unsupervised manner, we propose to additionally leverage semantic
information such as keypoint dynamics to guide the deformation learning. We
demonstrate our model with an example application of creating an expressive
animatable human avatar from a collection of 3D scans. Here, previous methods
mostly rely on variants of the linear blend skinning paradigm, which
fundamentally limits the expressivity of such models when dealing with complex
cloth appearances such as long skirts. We show the advantages of our dynamic
point field framework in terms of its representational power, learning
efficiency, and robustness to out-of-distribution novel poses.
