Hand tracking for immersive virtual reality: opportunities and challenges

• URL: http://arxiv.org/abs/2103.14853v1
• Date: Sat, 27 Mar 2021 09:28:47 GMT
• Title: Hand tracking for immersive virtual reality: opportunities and challenges
• Authors: Gavin Buckingham
• Abstract summary: Hand tracking has become an integral feature of recent generations of immersive virtual reality head-mounted displays. I outline what I see as the main possibilities for hand tracking to add value to immersive virtual reality. It is hoped that this paper serves as a roadmap for the development of best practices in the field for the development of subsequent generations of hand tracking and virtual reality technologies.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Hand tracking has become an integral feature of recent generations of immersive virtual reality head-mounted displays. With the widespread adoption of this feature, hardware engineers and software developers are faced with an exciting array of opportunities and a number of challenges, mostly in relation to the human user. In this article, I outline what I see as the main possibilities for hand tracking to add value to immersive virtual reality as well as some of the potential challenges in the context of the psychology and neuroscience of the human user. It is hoped that this paper serves as a roadmap for the development of best practices in the field for the development of subsequent generations of hand tracking and virtual reality technologies.

Related papers

• RealDex: Towards Human-like Grasping for Robotic Dexterous Hand [64.47045863999061]
  We introduce RealDex, a pioneering dataset capturing authentic dexterous hand grasping motions infused with human behavioral patterns. RealDex holds immense promise in advancing humanoid robot for automated perception, cognition, and manipulation in real-world scenarios.
  arXiv  Detail & Related papers  (2024-02-21T14:59:46Z)
• VR-GS: A Physical Dynamics-Aware Interactive Gaussian Splatting System in Virtual Reality [39.53150683721031]
  Our proposed VR-GS system represents a leap forward in human-centered 3D content interaction. The components of our Virtual Reality system are designed for high efficiency and effectiveness.
  arXiv  Detail & Related papers  (2024-01-30T01:28:36Z)
• Beyond Reality: The Pivotal Role of Generative AI in the Metaverse [98.1561456565877]
  This paper offers a comprehensive exploration of how generative AI technologies are shaping the Metaverse. We delve into the applications of text generation models like ChatGPT and GPT-3, which are enhancing conversational interfaces with AI-generated characters. We also examine the potential of 3D model generation technologies like Point-E and Lumirithmic in creating realistic virtual objects.
  arXiv  Detail & Related papers  (2023-07-28T05:44:20Z)
• Systematic Adaptation of Communication-focused Machine Learning Models from Real to Virtual Environments for Human-Robot Collaboration [1.392250707100996]
  This paper presents a systematic framework for the real to virtual adaptation using limited size of virtual dataset. Hand gestures recognition which has been a topic of much research and subsequent commercialization in the real world has been possible because of the creation of large, labelled datasets.
  arXiv  Detail & Related papers  (2023-07-21T03:24:55Z)
• Virtual Reality in Metaverse over Wireless Networks with User-centered Deep Reinforcement Learning [8.513938423514636]
  We introduce a multi-user VR computation offloading over wireless communication scenario. In addition, we devised a novel user-centered deep reinforcement learning approach to find a near-optimal solution.
  arXiv  Detail & Related papers  (2023-03-08T03:10:41Z)
• Privacy concerns from variances in spatial navigability in VR [0.0]
  Current Virtual Reality (VR) input devices make it possible to navigate a virtual environment and record immersive, personalized data regarding the user's movement and specific behavioral habits. In this article, the authors propose to investigate Machine Learning driven learning algorithms that try to learn with human users co-operatively and can be used to countermand existing privacy concerns in VR.
  arXiv  Detail & Related papers  (2023-02-06T01:48:59Z)
• Force-Aware Interface via Electromyography for Natural VR/AR Interaction [69.1332992637271]
  We design a learning-based neural interface for natural and intuitive force inputs in VR/AR. We show that our interface can decode finger-wise forces in real-time with 3.3% mean error, and generalize to new users with little calibration. We envision our findings to push forward research towards more realistic physicality in future VR/AR.
  arXiv  Detail & Related papers  (2022-10-03T20:51:25Z)
• Towards Everyday Virtual Reality through Eye Tracking [1.2691047660244335]
  Eye tracking is an emerging technology that helps to assess human behaviors in a real time and non-intrusive way. A significant scientific push towards everyday virtual reality has been completed with three main research contributions.
  arXiv  Detail & Related papers  (2022-03-29T16:09:37Z)
• Artificial Intelligence for the Metaverse: A Survey [66.57225253532748]
  We first deliver a preliminary of AI, including machine learning algorithms and deep learning architectures, and its role in the metaverse. We then convey a comprehensive investigation of AI-based methods concerning six technical aspects that have potentials for the metaverse. Several AI-aided applications, such as healthcare, manufacturing, smart cities, and gaming, are studied to be deployed in the virtual worlds.
  arXiv  Detail & Related papers  (2022-02-15T03:34:56Z)
• Robust Egocentric Photo-realistic Facial Expression Transfer for Virtual Reality [68.18446501943585]
  Social presence will fuel the next generation of communication systems driven by digital humans in virtual reality (VR) The best 3D video-realistic VR avatars that minimize the uncanny effect rely on person-specific (PS) models. This paper makes progress in overcoming these limitations by proposing an end-to-end multi-identity architecture.
  arXiv  Detail & Related papers  (2021-04-10T15:48:53Z)
• Visual Navigation Among Humans with Optimal Control as a Supervisor [72.5188978268463]
  We propose an approach that combines learning-based perception with model-based optimal control to navigate among humans. Our approach is enabled by our novel data-generation tool, HumANav. We demonstrate that the learned navigation policies can anticipate and react to humans without explicitly predicting future human motion.
  arXiv  Detail & Related papers  (2020-03-20T16:13:47Z)

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.