Full-Body Locomotion Reconstruction of Virtual Characters Using a Single IMU

TL;DR

This paper introduces a real-time method for reconstructing full-body virtual character locomotion from a single IMU using hierarchical hidden Markov models, improving accuracy and efficiency over previous techniques.

Contribution

The novel hierarchical multivariate hidden Markov model with reactive interpolation enables effective full-body motion reconstruction from minimal IMU data.

Findings

Works in real-time with reasonable frame rates

Reduces reconstruction errors compared to prior methods

Successfully predicts locomotion phases and interpolates motions

Abstract

This paper presents a method of reconstructing full-body locomotion sequences for virtual characters in real-time, using data from a single inertial measurement unit (IMU). This process can be characterized by its difficulty because of the need to reconstruct a high number of degrees of freedom (DOFs) from a very low number of DOFs. To solve such a complex problem, the presented method is divided into several steps. The user's full-body locomotion and the IMU's data are recorded simultaneously. Then, the data is preprocessed in such a way that would be handled more efficiently. By developing a hierarchical multivariate hidden Markov model with reactive interpolation functionality the system learns the structure of the motion sequences. Specifically, the phases of the locomotion sequence are assigned in the higher hierarchical level, and the frame structure of the motion sequences are assigned at the lower hierarchical level. During the runtime of the method, the forward algorithm is used for reconstructing the full-body motion of a virtual character. Firstly, the method predicts the phase where the input motion belongs (higher hierarchical level). Secondly, the method predicts the closest trajectories and their progression and interpolates the most probable of them to reconstruct the virtual character's full-body motion (lower hierarchical level). Evaluating the proposed method shows that it works on reasonable framerates and minimizes the reconstruction errors compared with previous approaches.