Lower body control of a semi-autonomous avatar in Virtual Reality: Balance and Locomotion of a 3D Bipedal Model

Abstract

Animated virtual humans may rely on full-body tracking system to reproduce user motions. In this paper, we reduce tracking to the upper-body and reconstruct the lower body to follow autonomously its upper counterpart. Doing so reduces the number of sensors required, making the application of virtual humans simpler and cheaper. It also enable deployment in cluttered scenes where the lower body is often hidden. The contribution here is the inversion of the well-known capture problem for bipedal walking. It determines footsteps rather than center-of-mass motions and yet can be solved with an off-the-shelf capture problem solver. The quality of our method is assessed in real-time tracking experiments on a wide variety of movements.

LoBSTr: Real‐time Lower‐body Pose Prediction from Sparse Upper‐body Tracking Signals

“…As a blended motion, the resulting motion looks natural but its scope is limited by the predefined animations. A balance controlbased method [TCW19] reconstructs static pose and locomotion of the lower-body according to the tracked upper-body joints. Specifically, the target Zero Moment Point (ZMP) trajectory is determined from the upper-body motion, and the full-body animation is generated to realize the ZMP trajectory.…”