Motion Compression for Telepresence Locomotion

Su, Jianbo

doi:10.1162/pres.16.4.385

Cited by 37 publications

(28 citation statements)

References 12 publications

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“…In the past, prediction for redirected walking was done by estimating a single future path which was then transformed to keep the user inside the tracking space [5,16,22,24]. These predictions were made based on the user's current viewing direction, direction of movement, past movement or a combination thereof.…”

Section: Prediction For Redirected Walkingmentioning

confidence: 99%

“…These predictions were made based on the user's current viewing direction, direction of movement, past movement or a combination thereof. The resulting prediction is a single straight line path with the exception of Su et al [24] who fitted a curved path based on the user's past path. However, a model predictive control planner for redirected walking can consider multiple branching paths in its planning process, and to take full advantage of its potential we require a prediction that provides multiple future paths together with their respective probability.…”

Section: Prediction For Redirected Walkingmentioning

confidence: 99%

“…In order to use this additional information, a number of prediction approaches based on the user's movement were developed. Su et al proposed a prediction for telepresence motion-compression by using linear regression of the user's past path to extrapolate his movements into the future [24]. Nescher et al used a double exponential smoother instead of the regression to smooth the gait-induced head movements and to estimate the intended movement direction [15].…”

Section: Related Workmentioning

confidence: 99%

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Eye tracking for locomotion prediction in redirected walking

Zank

Kunz

2016

2016 IEEE Symposium on 3D User Interfaces (3DUI)

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Model predictive control was shown to be a powerful tool for Redirected Walking when used to plan and select future redirection techniques. However, to use it effectively, a good prediction of the user's future actions is crucial. Traditionally, this prediction is made based on the user's position or current direction of movement. In the area of cognitive sciences however, it was shown that a person's gaze can also be highly indicative of his intention in both selection and navigation tasks.In this paper, this effect is used the first time to predict a user's locomotion target during goal-directed locomotion in an immersive virtual environment. After discussing the general implications and challenges of using eye tracking for prediction in a locomotion context, we propose a prediction method for a user's intended locomotion target. This approach is then compared with position based approaches in terms of prediction time and accuracy based on data gathered in an experiment.The results show that, in certain situations, eye tracking allows an earlier prediction compared approaches currently used for redirected walking. However, other recently published prediction methods that are based on the user's position perform almost as well as the eye tracking based approaches presented in this paper.

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Section: Prediction For Redirected Walkingmentioning

confidence: 99%

Section: Prediction For Redirected Walkingmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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Eye tracking for locomotion prediction in redirected walking

Zank

Kunz

2016

2016 IEEE Symposium on 3D User Interfaces (3DUI)

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“…Engel et al [3] proposed a psychophysically calibrated controller that employs a cost function based on the noticeability of redirection. Nitzsche et al [12] and Su et al [19] suggested an approach that dynamically minimizes the introduced curvature, but does not make imperceptibility a primary goal. A new way for planning RETs was proposed by Zmuda et al [25].…”

Section: Related Workmentioning

confidence: 99%

Planning redirection techniques for optimal free walking experience using model predictive control

Nescher

Huang

Kunz

2014

2014 IEEE Symposium on 3D User Interfaces (3DUI)

121

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Redirected Walking (RDW) is a technique that allows exploring immersive virtual environments by real walking in a small physical room. RDW employs so-called redirection techniques (RETs) to manipulate the user's real world trajectory in such a way that he remains within the boundaries of the physical room. Different RETs were suggested and evaluated in the past. In addition, steering algorithms were proposed that apply a limited set of RETs to redirect a user away from the physical room's boundaries.Within this paper, a generalized approach to planning and applying RETs is presented. It is capable of dynamically selecting suitable RETs and also controlling parameters like their strengths. The problem of steering a user in a small physical room using RETs is formulated as an optimal control problem. This allows applying an efficient probabilistic planning algorithm to maximize the free walking experience. The proposed algorithm uses a map of the virtual environment to continuously determine the optimal RET that has to be applied next.The suggested algorithm is evaluated within a user study and compared to a state-of-the-art steering algorithm. Results show that for the given virtual environment, it is able to reduce the number of collisions with the room boundaries by 41% and furthermore reduces the amount of applied redirections significantly.

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“…Another approach for the user's future path was introduced by [11]. Here, the future walking direction is simply determined by a linear extrapolation of the user's previous path.…”

Section: Introductionmentioning

confidence: 99%

Using Head Tracking Data for Robust Short Term Path Prediction of Human Locomotion

Nescher

Künz

2013

Lecture Notes in Computer Science

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Modern interactive environments like virtual reality simulators or augmented reality systems often require reliable information about a user's future intention in order to increase their immersion and usefulness. For many of such systems, where human locomotion is an essential way of interaction, knowing a user's future walking direction provides relevant information.This paper explains how head tracking data can be used to retrieve a person's intended direction of walking. The goal is to provide a reliable and stable path prediction of human locomotion that holds for a few seconds. Using 6 degrees of freedom head tracking data, the head orientation and the head's movement direction can be derived. Within a user study it is shown that such raw tracking data provides poor prediction results mainly due to noise from gait oscillations. Hence, smoothing filters have to be applied to the data to increase the reliability and robustness of a predictor.Results of the user study show that double exponential smoothing of a person's walking direction data in combination with an initialization using the head orientation provides a reliable short term path predictor with high robustness.

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Motion Compression for Telepresence Locomotion

Cited by 37 publications

References 12 publications

Eye tracking for locomotion prediction in redirected walking

Eye tracking for locomotion prediction in redirected walking

Planning redirection techniques for optimal free walking experience using model predictive control

Using Head Tracking Data for Robust Short Term Path Prediction of Human Locomotion

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