  Constructing and animating humans is an important component for building
virtual worlds in a wide variety of applications such as virtual reality or
robotics testing in simulation. As there are exponentially many variations of
humans with different shape, pose and clothing, it is critical to develop
methods that can automatically reconstruct and animate humans at scale from
real world data. Towards this goal, we represent the pedestrian's shape, pose
and skinning weights as neural implicit functions that are directly learned
from data. This representation enables us to handle a wide variety of different
pedestrian shapes and poses without explicitly fitting a human parametric body
model, allowing us to handle a wider range of human geometries and topologies.
We demonstrate the effectiveness of our approach on various datasets and show
that our reconstructions outperform existing state-of-the-art methods.
Furthermore, our re-animation experiments show that we can generate 3D human
animations at scale from a single RGB image (and/or an optional LiDAR sweep) as
input.
