  Virtual telepresence is the future of online communication. Clothing is an
essential part of a person's identity and self-expression. Yet, ground truth
data of registered clothes is currently unavailable in the required resolution
and accuracy for training telepresence models for realistic cloth animation.
Here, we propose an end-to-end pipeline for building drivable representations
for clothing. The core of our approach is a multi-view patterned cloth tracking
algorithm capable of capturing deformations with high accuracy. We further rely
on the high-quality data produced by our tracking method to build a Garment
Avatar: an expressive and fully-drivable geometry model for a piece of
clothing. The resulting model can be animated using a sparse set of views and
produces highly realistic reconstructions which are faithful to the driving
signals. We demonstrate the efficacy of our pipeline on a realistic virtual
telepresence application, where a garment is being reconstructed from two
views, and a user can pick and swap garment design as they wish. In addition,
we show a challenging scenario when driven exclusively with body pose, our
drivable garment avatar is capable of producing realistic cloth geometry of
significantly higher quality than the state-of-the-art.
