Predictive Context-Awareness for Full-Immersive Multiuser Virtual Reality with Redirected Walking

• URL: http://arxiv.org/abs/2303.17907v4
• Date: Mon, 29 May 2023 13:41:43 GMT
• Title: Predictive Context-Awareness for Full-Immersive Multiuser Virtual Reality with Redirected Walking
• Authors: Filip Lemic, Jakob Struye, Thomas Van Onsem, Jeroen Famaey, Xavier Costa Perez
• Abstract summary: Future VR systems will require supporting wireless networking infrastructures operating in millimeter Wave (mmWave) frequencies. We propose the use of predictive context-awareness to optimize transmitter and receiver-side beamforming and beamsteering. We show that Long Short-Term Memory (LSTM) networks feature promising accuracy in predicting lateral movements.
• Score: 5.393569497095572
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The advancement of Virtual Reality (VR) technology is focused on improving its immersiveness, supporting multiuser Virtual Experiences (VEs), and enabling users to move freely within their VEs while remaining confined to specialized VR setups through Redirected Walking (RDW). To meet their extreme data-rate and latency requirements, future VR systems will require supporting wireless networking infrastructures operating in millimeter Wave (mmWave) frequencies that leverage highly directional communication in both transmission and reception through beamforming and beamsteering. We propose the use of predictive context-awareness to optimize transmitter and receiver-side beamforming and beamsteering. By predicting users' short-term lateral movements in multiuser VR setups with Redirected Walking (RDW), transmitter-side beamforming and beamsteering can be optimized through Line-of-Sight (LoS) "tracking" in the users' directions. At the same time, predictions of short-term orientational movements can be utilized for receiver-side beamforming for coverage flexibility enhancements. We target two open problems in predicting these two context information instances: i) predicting lateral movements in multiuser VR settings with RDW, and ii) generating synthetic head rotation datasets for training orientational movements predictors. Our experimental results demonstrate that Long Short-Term Memory (LSTM) networks feature promising accuracy in predicting lateral movements, and context-awareness stemming from VEs further enhances this accuracy. Additionally, we show that a TimeGAN-based approach for orientational data generation can create synthetic samples that closely match experimentally obtained ones.

Related papers

• E-Motion: Future Motion Simulation via Event Sequence Diffusion [86.80533612211502]
  Event-based sensors may potentially offer a unique opportunity to predict future motion with a level of detail and precision previously unachievable. We propose to integrate the strong learning capacity of the video diffusion model with the rich motion information of an event camera as a motion simulation framework. Our findings suggest a promising direction for future research in enhancing the interpretative power and predictive accuracy of computer vision systems.
  arXiv  Detail & Related papers  (2024-10-11T09:19:23Z)
• Tremor Reduction for Accessible Ray Based Interaction in VR Applications [0.0]
  Many traditional 2D interface interaction methods have been directly converted to work in a VR space with little alteration to the input mechanism. In this paper we propose the use of a low pass filter, to normalize user input noise, alleviating fine motor requirements during ray-based interaction.
  arXiv  Detail & Related papers  (2024-05-12T17:07:16Z)
• Adapting to Length Shift: FlexiLength Network for Trajectory Prediction [53.637837706712794]
  Trajectory prediction plays an important role in various applications, including autonomous driving, robotics, and scene understanding. Existing approaches mainly focus on developing compact neural networks to increase prediction precision on public datasets, typically employing a standardized input duration. We introduce a general and effective framework, the FlexiLength Network (FLN), to enhance the robustness of existing trajectory prediction against varying observation periods.
  arXiv  Detail & Related papers  (2024-03-31T17:18:57Z)
• Radar-Based Recognition of Static Hand Gestures in American Sign Language [17.021656590925005]
  This study explores the efficacy of synthetic data generated by an advanced radar ray-tracing simulator. The simulator employs an intuitive material model that can be adjusted to introduce data diversity. Despite exclusively training the NN on synthetic data, it demonstrates promising performance when put to the test with real measurement data.
  arXiv  Detail & Related papers  (2024-02-20T08:19:30Z)
• Federated Multi-View Synthesizing for Metaverse [52.59476179535153]
  The metaverse is expected to provide immersive entertainment, education, and business applications. Virtual reality (VR) transmission over wireless networks is data- and computation-intensive. We have developed a novel multi-view synthesizing framework that can efficiently provide synthesizing, storage, and communication resources for wireless content delivery in the metaverse.
  arXiv  Detail & Related papers  (2023-12-18T13:51:56Z)
• TLControl: Trajectory and Language Control for Human Motion Synthesis [68.09806223962323]
  We present TLControl, a novel method for realistic human motion synthesis. It incorporates both low-level Trajectory and high-level Language semantics controls. It is practical for interactive and high-quality animation generation.
  arXiv  Detail & Related papers  (2023-11-28T18:54:16Z)
• Joint Sensing, Communication, and AI: A Trifecta for Resilient THz User Experiences [118.91584633024907]
  A novel joint sensing, communication, and artificial intelligence (AI) framework is proposed so as to optimize extended reality (XR) experiences over terahertz (THz) wireless systems.
  arXiv  Detail & Related papers  (2023-04-29T00:39:50Z)
• F-RDW: Redirected Walking with Forecasting Future Position [2.257416403770908]
  We propose a novel mechanism F-RDW that is twofold: (1) forecasts the future information of a user in the virtual space without any assumptions, and (2) fuse this information while maneuvering existing RDW methods. The backbone of the first step is an LSTM-based model that ingests the user's spatial and eye-tracking data to predict the user's future position in the virtual space. We prove that the proposed mechanism significantly reduces the number of resets and increases the traveled distance between resets.
  arXiv  Detail & Related papers  (2023-04-07T06:37:17Z)
• WiserVR: Semantic Communication Enabled Wireless Virtual Reality Delivery [12.158124978097982]
  We propose a novel framework, namely WIreless SEmantic deliveRy for VR (WiserVR), for delivering consecutive 360deg video frames to VR users. Deep learning-based multiple modules are well-devised for the transceiver in WiserVR to realize high-performance feature extraction and semantic recovery.
  arXiv  Detail & Related papers  (2022-11-02T16:22:41Z)
• Short-Term Trajectory Prediction for Full-Immersive Multiuser Virtual Reality with Redirected Walking [6.622115542749609]
  Full-immersive multiuser Virtual Reality (VR) envisions supporting unconstrained mobility of the users in the virtual worlds. We show that Gated Recurrent Unit (GRU) networks, another candidate from the RNN family, generally outperform the traditionally utilized LSTMs. Second, we show that context from a virtual world can enhance the accuracy of the prediction if used as an additional input feature.
  arXiv  Detail & Related papers  (2022-07-15T15:09:07Z)
• Feeling of Presence Maximization: mmWave-Enabled Virtual Reality Meets Deep Reinforcement Learning [76.46530937296066]
  This paper investigates the problem of providing ultra-reliable and energy-efficient virtual reality (VR) experiences for wireless mobile users. To ensure reliable ultra-high-definition (UHD) video frame delivery to mobile users, a coordinated multipoint (CoMP) transmission technique and millimeter wave (mmWave) communications are exploited.
  arXiv  Detail & Related papers  (2021-06-03T08:35:10Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.