Full-Body Locomotion Reconstruction of Virtual Characters Using a Single Inertial Measurement Unit
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 (DOF...
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doaj-47c86924441e4843863cc59e093351fd2020-11-24T21:48:27ZengMDPI AGSensors1424-82202017-11-011711258910.3390/s17112589s17112589Full-Body Locomotion Reconstruction of Virtual Characters Using a Single Inertial Measurement UnitChristos Mousas0Department of Computer Science, Southern Illinois University, 1230 Lincoln Drive, Mail Code 4511, Carbondale, IL 62901, USAThis 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.https://www.mdpi.com/1424-8220/17/11/2589character animationmotion datalocomotion reconstructionHMMIMU |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Christos Mousas |
spellingShingle |
Christos Mousas Full-Body Locomotion Reconstruction of Virtual Characters Using a Single Inertial Measurement Unit Sensors character animation motion data locomotion reconstruction HMM IMU |
author_facet |
Christos Mousas |
author_sort |
Christos Mousas |
title |
Full-Body Locomotion Reconstruction of Virtual Characters Using a Single Inertial Measurement Unit |
title_short |
Full-Body Locomotion Reconstruction of Virtual Characters Using a Single Inertial Measurement Unit |
title_full |
Full-Body Locomotion Reconstruction of Virtual Characters Using a Single Inertial Measurement Unit |
title_fullStr |
Full-Body Locomotion Reconstruction of Virtual Characters Using a Single Inertial Measurement Unit |
title_full_unstemmed |
Full-Body Locomotion Reconstruction of Virtual Characters Using a Single Inertial Measurement Unit |
title_sort |
full-body locomotion reconstruction of virtual characters using a single inertial measurement unit |
publisher |
MDPI AG |
series |
Sensors |
issn |
1424-8220 |
publishDate |
2017-11-01 |
description |
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. |
topic |
character animation motion data locomotion reconstruction HMM IMU |
url |
https://www.mdpi.com/1424-8220/17/11/2589 |
work_keys_str_mv |
AT christosmousas fullbodylocomotionreconstructionofvirtualcharactersusingasingleinertialmeasurementunit |
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