  Currently it requires an artist to create 3D human avatars with realistic
clothing that can move naturally. Despite progress on 3D scanning and modeling
of human bodies, there is still no technology that can easily turn a static
scan into an animatable avatar. Automating the creation of such avatars would
enable many applications in games, social networking, animation, and AR/VR to
name a few. The key problem is one of representation. Standard 3D meshes are
widely used in modeling the minimally-clothed body but do not readily capture
the complex topology of clothing. Recent interest has shifted to implicit
surface models for this task but they are computationally heavy and lack
compatibility with existing 3D tools. What is needed is a 3D representation
that can capture varied topology at high resolution and that can be learned
from data. We argue that this representation has been with us all along -- the
point cloud. Point clouds have properties of both implicit and explicit
representations that we exploit to model 3D garment geometry on a human body.
We train a neural network with a novel local clothing geometric feature to
represent the shape of different outfits. The network is trained from 3D point
clouds of many types of clothing, on many bodies, in many poses, and learns to
model pose-dependent clothing deformations. The geometry feature can be
optimized to fit a previously unseen scan of a person in clothing, enabling the
scan to be reposed realistically. Our model demonstrates superior quantitative
and qualitative results in both multi-outfit modeling and unseen outfit
animation. The code is available for research purposes.
