  This paper focuses on the task of 4D shape reconstruction from a sequence of
point clouds. Despite the recent success achieved by extending deep implicit
representations into 4D space, it is still a great challenge in two respects,
i.e. how to design a flexible framework for learning robust spatio-temporal
shape representations from 4D point clouds, and develop an efficient mechanism
for capturing shape dynamics. In this work, we present a novel pipeline to
learn a temporal evolution of the 3D human shape through spatially continuous
transformation functions among cross-frame occupancy fields. The key idea is to
parallelly establish the dense correspondence between predicted occupancy
fields at different time steps via explicitly learning continuous displacement
vector fields from robust spatio-temporal shape representations. Extensive
comparisons against previous state-of-the-arts show the superior accuracy of
our approach for 4D human reconstruction in the problems of 4D shape
auto-encoding and completion, and a much faster network inference with about 8
times speedup demonstrates the significant efficiency of our approach. The
trained models and implementation code are available at
https://github.com/tangjiapeng/LPDC-Net.
