  Today's Mixed Reality head-mounted displays track the user's head pose in
world space as well as the user's hands for interaction in both Augmented
Reality and Virtual Reality scenarios. While this is adequate to support user
input, it unfortunately limits users' virtual representations to just their
upper bodies. Current systems thus resort to floating avatars, whose limitation
is particularly evident in collaborative settings. To estimate full-body poses
from the sparse input sources, prior work has incorporated additional trackers
and sensors at the pelvis or lower body, which increases setup complexity and
limits practical application in mobile settings. In this paper, we present
AvatarPoser, the first learning-based method that predicts full-body poses in
world coordinates using only motion input from the user's head and hands. Our
method builds on a Transformer encoder to extract deep features from the input
signals and decouples global motion from the learned local joint orientations
to guide pose estimation. To obtain accurate full-body motions that resemble
motion capture animations, we refine the arm joints' positions using an
optimization routine with inverse kinematics to match the original tracking
input. In our evaluation, AvatarPoser achieved new state-of-the-art results in
evaluations on large motion capture datasets (AMASS). At the same time, our
method's inference speed supports real-time operation, providing a practical
interface to support holistic avatar control and representation for Metaverse
applications.
