Either RGB images or inertial signals have been used for the task of motion capture
(mocap), but combining them together is a new and interesting topic. We believe that the …

We present a method for reproducing complex multi-character interactions for physically
simulated humanoid characters using deep reinforcement learning. Our method learns …

Replicating a user's pose from only wearable sensors is important for many AR/VR
applications. Most existing methods for motion tracking avoid environment interaction apart …

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 …

Motion retargeting is a fundamental problem in computer graphics and computer vision.
Existing approaches usually have many strict requirements, such as the source-target …

Estimating full-body human motion via sparse tracking signals from head-mounted displays
and hand controllers in 3D scenes is crucial to applications in AR/VR. One of the biggest …

We present a novel generative model, called Bidirectional GaitNet, that learns the
relationship between human anatomy and its gait. The simulation model of human anatomy …

The most popular type of devices used to track a user's posture in a virtual reality experience
consists of a head-mounted display and two controllers held in both hands. However, due to …

We propose a learning‐based approach for full‐body pose reconstruction from extremely
sparse upper body tracking data, obtained from a virtual reality (VR) device. We leverage a …

Accurate full-body pose estimation across diverse actions in a user-friendly and location-
agnostic manner paves the way for interactive applications in realms like sports, fitness, and …