We created a virtual reality version of a 1983 performance by Dire Straits, this being a highly complex scenario consisting of both the virtual band performance and the appearance and behaviour of the virtual audience surrounding the participants. Our goal was to understand the responses of participants, and to learn how this type of scenario might be improved for later reconstructions of other concerts. To understand the responses of participants we carried out two studies which used sentiment analysis of texts written by the participants. Study 1 (n = 25) (Beacco et al. in IEEE Virtual Reality: 538–545, 2021) had the unexpected finding that negative sentiment was caused by the virtual audience, where e.g. some participants were fearful of being harassed by audience members. In Study 2 (n = 26) notwithstanding some changes, the audience again led to negative sentiment—e.g. a feeling of being stared at. For Study 2 we compared sentiment with questionnaire scores, finding that the illusion of being at the concert was associated with positive sentiment for males but negative for females. Overall, we found sentiment was dominated by responses to the audience rather than the band. Participants had been placed in an unusual situation, being alone at a concert, surrounded by strangers, who seemed to pose a social threat for some of them. We relate our findings to the concept of Plausibility, the illusion that events and situations in the VR are really happening. The results indicate high Plausibility, since the negative sentiment, for example in response to being started at, only makes sense if the events are experienced as actually happening. We conclude with the need for co-design of VR scenarios, and the use of sentiment analysis in this process, rather than sole reliance on concepts proposed by researchers, typically expressed through questionnaires, which may not reflect the experiences of participants.

This work deals with the automatic 3D reconstruction of objects from frontal RGB images. This aims at a better understanding of the reconstruction of 3D objects from RGB images and their use in immersive virtual environments. We propose a complete workflow that can be easily adapted to almost any other family of rigid objects. To explain and validate our method, we focus on guitars. First, we detect and segment the guitars present in the image using semantic segmentation methods based on convolutional neural networks. In a second step, we perform the final 3D reconstruction of the guitar by warping the rendered depth maps of a fitted 3D template in 2D image space to match the input silhouette. We validated our method by obtaining guitar reconstructions from real input images and renders of all guitar models available in the ShapeNet database. Numerical results for different object families were obtained by computing standard mesh evaluation metrics such as Intersection over Union, Chamfer Distance, and the F-score. The results of this study show that our method can automatically generate high-quality 3D object reconstructions from frontal images using various segmentation and 3D reconstruction techniques.

VR United is a virtual reality application that we have developed to support multiple people simultaneously interacting in the same environment. Each person is represented with a virtual body that looks like themselves. Such immersive shared environments have existed and been the subject of research for the past 30 years. Here, we demonstrate how VR United meets criteria for successful interaction, where a journalist from the Financial Times in London interviewed a professor in New York for two hours. The virtual location of the interview was a restaurant, in line with the series of interviews published as “Lunch with the FT.” We show how the interview was successful, as a substitute for a physically present one. The article based on the interview was published in the Financial Times as normal for the series. We finally consider the future development of such systems, including some implications for immersive journalism.

We introduce an ultra-compact, single-neuron-per-class end-to-end readout for binary classification of noisy, image-encoded sensor time series. The approach compares a linear single-unit perceptron (E2E-MLP-1) with a resonate-and-fire (RAF) neuron (E2E-RAF-1), which merges feature selection and decision-making in a single block. Beyond empirical evaluation, we provide a mathematical analysis of the RAF readout: starting from its subthreshold ordinary differential equation, we derive the transfer function $H\left(\mathrm{j}\omega \right)$, characterize the frequency response, and relate the output signal-to-noise ratio (SNR) to ${\left|H\left(\mathrm{j}\omega \right)\right|}^2$ and the noise power spectral density $S_n\left(\omega \right)\propto {\omega }^{\alpha }$ (brown, pink, and blue noise). We present a stable discrete-time implementation compatible with surrogate gradient training and discuss the associated stability constraints. As a case study, we classify walk-in-place (WIP) in a virtual reality (VR) environment, a vision-based motion encoding (72 × 56 grayscale) derived from 3D trajectories, comprising 44,084 samples from 15 participants. On clean data, both single-neuron-per-class models approach ceiling accuracy. At the same time, under colored noise, the RAF readout yields consistent gains (typically +5–8% absolute accuracy at medium/high perturbations), indicative of intrinsic band-selective filtering induced by resonance. With ∼8 k parameters and sub-2 ms inference on commodity graphical processing units (GPUs), the RAF readout provides a mathematically grounded, robust, and efficient alternative for stochastic signal processing across domains, with virtual reality locomotion used here as an illustrative validation.

The metaverse is posited as the next evolution of human–computer interaction, promising unprecedented social presence through realistic avatars. Yet, realism alone is insufficient: As photorealism increases, so does the feasibility of identity spoofing (such as deepfakes) and real-time impersonation. Realism concerns how closely an avatar aligns with a user’s appearance and behavior; verification concerns how reliably we can establish – and continuously maintain – the link between the avatar and the human who claims it. This chapter confronts that tension by surveying the psychological foundations of presence, state-of-the-art AI methods for photorealistic, behaviorally congruent avatars, and the resulting trust crisis in multiuser environments. We then introduce the AI-driven Continuous Trust Verification Framework: a layered architecture that integrates active/passive biometrics, behavioral signatures, decentralized cryptographic proofs, and interoperability mechanisms as the necessary components of a robust identity fabric. Finally, we examine the transdisciplinary implications and argue that embodied presence and verified trust are inseparable: Without continuous verification, realism erodes plausibility; with it, realism can safely underpin governance, commerce, and public services in virtual worlds.