Making Resets away from Targets: POI aware Redirected Walking

Xu, Sen‐Zhe; Liu, Tianqi; Liu, Jiahong; Zollmann, Stefanie; Zhang, Song-Hai

doi:10.1109/tvcg.2022.3203095

Cited by 12 publications

(8 citation statements)

References 35 publications

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“…Cheng et al 22 designed a tower climbing game to provide an infinite walking experience. Zhang and Xu et al manipulate the reset to optimize the interruption experience 23‐26 …”

Section: Related Workmentioning

confidence: 99%

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DreamWalk: Dynamic remapping and multiperspectivity for large‐scale redirected walking

Xiong

Chen

et al. 2023

Computer Animation & Virtual

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SummaryRedirected walking (RDW) provides an immersive user experience in virtual reality applications. In RDW, the size of the physical play area is limited, which makes it challenging to design the virtual path in a larger virtual space. Mainstream RDW approaches rigidly manipulate gains to guide the user to follow predetermined rules. However, these methods may cause simulator sickness, boundary collision, and reset. Static mapping approaches warp the virtual path through expensive vertex replacement in the stage of model pre‐processing. They are restricted to narrow spaces with non‐looping pathways, partition walls, and planar surfaces. These methods fail to provide a smooth walking experience for large‐scale open scenes. To tackle these problems, we propose a novel approach that dynamically redirects the user to walk in a non‐linear virtual space. More specifically, we propose a Bezier‐curve‐based mapping algorithm to warp the virtual space dynamically and apply multiperspective fusion for visualization augmentation. We conduct comparable experiments to show its superiority over state‐of‐the‐art large‐scale redirected walking approaches on our self‐collected photogrammetry dataset.

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“…Cheng et al 22 designed a tower climbing game to provide an infinite walking experience. Zhang and Xu et al manipulate the reset to optimize the interruption experience 23‐26 …”

Section: Related Workmentioning

confidence: 99%

“…Zhang and Xu et al manipulate the reset to optimize the interruption experience. [23][24][25][26] There are few large-scale RDW approaches. The Giant 2 manipulates the avatar size and speed gains.…”

Section: Related Workmentioning

confidence: 99%

DreamWalk: Dynamic remapping and multiperspectivity for large‐scale redirected walking

Xiong

Chen

et al. 2023

Computer Animation & Virtual

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“…In addition to resets that rotate in place before colliding with a boundary, various methods have also been studied, such as out-of-place resetting (OPR) [46], which allows the users to redirect optimal physical locations to continue walking when performing a reset. Another method is to perform a reset even if there is no collision, to avoid performing a reset directly in front of a point of interest (POI) set as a target [44]. However, to date, reset techniques have primarily been studied in a single-user environment.…”

Section: Reset Techniquementioning

confidence: 99%

“…In this case, the loss of immersion can be minimized by using a reset technique that rotates users in the direction based on a specific rule or optimized for the environment [34]. By proposing different rules for the reset technique that determine the reset direction, studies have been conducted to reduce the number of resets [36,40,44,46]. In addition, a study has recently been proposed to optimize previous reset techniques with a local algorithm according to the shape of the physical space [45].…”

Section: Introductionmentioning

confidence: 99%

Optimal Planning for Redirected Walking Based on Reinforcement Learning in Multi-user Environment with Irregularly Shaped Physical Space

Lee

Cho

Min

et al. 2020

2020 IEEE Conference on Virtual Reality and 3D User Interfaces (VR)

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Fig. 1: Reset control during multi-user RDW experience in a physical space with a circle obstacle in the center: (a) Reset-to-Center (R2C): the user unconditionally reset to the center of the physical space without considering other users or obstacle in the center. (b) Reset-to-Gradient (R2G): the user reset to the direction of the sum of the force that is acted on them by the boundary of the physical space, obstacles, and other users. (c) Multi-user Reset Controller (MRC): the user reset to the optimal direction determined by a model trained with information about the user's physical space.

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“…By proposing different rules for the reset technique that determine the reset direction, studies have been conducted to reduce the number of resets [6], [10], [11], [12]. In addition, a study has recently been proposed to optimize previous reset techniques with a local algorithm according to the shape of the physical space [13].…”

mentioning

confidence: 99%

MARR : A Multi-Agent Reinforcement Resetter for Redirected Walking

Lee,

Jeon,

Cho

et al. 2024

IEEE Trans. Visual. Comput. Graphics

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The reset technique of Redirected Walking (RDW) forcibly reorients the user's direction overtly to avoid collisions with boundaries, obstacles, or other users in the physical space. However, excessive resetting can decrease the user's sense of immersion and presence. Several RDW studies have been conducted to address this issue. Among them, much research has been done on reset techniques that reduce the number of resets by devising reset direction rules or optimizing them for a given environment. However, existing optimization studies on reset techniques have mainly focused on a single-user environment. In a multi-user environment, the dynamic movement of other users and static obstacles in the physical space increase the possibility of resetting. In this study, we propose Multi-Agent Reinforcement Resetter (MARR), which resets the user taking into account both physical obstacles and multi-user movement to minimize the number of resets. MARR is trained using multiagent reinforcement learning to determine the optimal reset direction in different environments. This approach allows MARR to effectively account for different environmental contexts, including arbitrary physical obstacles and the dynamic movements of other users in the same physical space. We compared MARR to other reset technologies through simulation tests and user studies, and found that MARR outperformed the existing methods. MARR improved performance by learning the optimal reset direction for each subtle technique used in training. MARR has the potential to be applied to new subtle techniques proposed in the future. Overall, our study confirmed that MARR is an effective reset technique in multi-user environments.

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Making Resets away from Targets: POI aware Redirected Walking

Cited by 12 publications

References 35 publications

DreamWalk: Dynamic remapping and multiperspectivity for large‐scale redirected walking

DreamWalk: Dynamic remapping and multiperspectivity for large‐scale redirected walking

Optimal Planning for Redirected Walking Based on Reinforcement Learning in Multi-user Environment with Irregularly Shaped Physical Space

MARR : A Multi-Agent Reinforcement Resetter for Redirected Walking

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