  Avatars are important to create interactive and immersive experiences in
virtual worlds. One challenge in animating these characters to mimic a user's
motion is that commercial AR/VR products consist only of a headset and
controllers, providing very limited sensor data of the user's pose. Another
challenge is that an avatar might have a different skeleton structure than a
human and the mapping between them is unclear. In this work we address both of
these challenges. We introduce a method to retarget motions in real-time from
sparse human sensor data to characters of various morphologies. Our method uses
reinforcement learning to train a policy to control characters in a physics
simulator. We only require human motion capture data for training, without
relying on artist-generated animations for each avatar. This allows us to use
large motion capture datasets to train general policies that can track unseen
users from real and sparse data in real-time. We demonstrate the feasibility of
our approach on three characters with different skeleton structure: a dinosaur,
a mouse-like creature and a human. We show that the avatar poses often match
the user surprisingly well, despite having no sensor information of the lower
body available. We discuss and ablate the important components in our
framework, specifically the kinematic retargeting step, the imitation, contact
and action reward as well as our asymmetric actor-critic observations. We
further explore the robustness of our method in a variety of settings including
unbalancing, dancing and sports motions.
