  Accurate and reliable human motion reconstruction is crucial for creating
natural interactions of full-body avatars in Virtual Reality (VR) and
entertainment applications. As the Metaverse and social applications gain
popularity, users are seeking cost-effective solutions to create full-body
animations that are comparable in quality to those produced by commercial
motion capture systems. In order to provide affordable solutions, though, it is
important to minimize the number of sensors attached to the subject's body.
Unfortunately, reconstructing the full-body pose from sparse data is a heavily
under-determined problem. Some studies that use IMU sensors face challenges in
reconstructing the pose due to positional drift and ambiguity of the poses. In
recent years, some mainstream VR systems have released 6-degree-of-freedom
(6-DoF) tracking devices providing positional and rotational information.
Nevertheless, most solutions for reconstructing full-body poses rely on
traditional inverse kinematics (IK) solutions, which often produce
non-continuous and unnatural poses. In this article, we introduce SparsePoser,
a novel deep learning-based solution for reconstructing a full-body pose from a
reduced set of six tracking devices. Our system incorporates a
convolutional-based autoencoder that synthesizes high-quality continuous human
poses by learning the human motion manifold from motion capture data. Then, we
employ a learned IK component, made of multiple lightweight feed-forward neural
networks, to adjust the hands and feet toward the corresponding trackers. We
extensively evaluate our method on publicly available motion capture datasets
and with real-time live demos. We show that our method outperforms
state-of-the-art techniques using IMU sensors or 6-DoF tracking devices, and
can be used for users with different body dimensions and proportions.
