  This paper presents a novel approach 4DRecons that takes a single camera
RGB-D sequence of a dynamic subject as input and outputs a complete textured
deforming 3D model over time. 4DRecons encodes the output as a 4D neural
implicit surface and presents an optimization procedure that combines a data
term and two regularization terms. The data term fits the 4D implicit surface
to the input partial observations. We address fundamental challenges in fitting
a complete implicit surface to partial observations. The first regularization
term enforces that the deformation among adjacent frames is as rigid as
possible (ARAP). To this end, we introduce a novel approach to compute
correspondences between adjacent textured implicit surfaces, which are used to
define the ARAP regularization term. The second regularization term enforces
that the topology of the underlying object remains fixed over time. This
regularization is critical for avoiding self-intersections that are typical in
implicit-based reconstructions. We have evaluated the performance of 4DRecons
on a variety of datasets. Experimental results show that 4DRecons can handle
large deformations and complex inter-part interactions and outperform
state-of-the-art approaches considerably.
