Social Interaction With Agents and Avatars in Immersive Virtual Environments: A Survey

Immersive virtual reality technologies are used in a wide range of fields such as training, education, health, and research. Many of these applications include virtual humans that are classified into avatars and agents. An overview of the applications and the advantages of immersive virtual reality and virtual humans is presented in this survey, as well as the basic concepts and terminology. To be effective, many virtual reality applications require that the users perceive and react socially to the virtual humans in a realistic manner. Numerous studies show that people can react socially to virtual humans; however, this is not always the case. This survey provides an overview of the main findings regarding the factors affecting the social interaction with virtual humans within immersive virtual environments. Finally, this survey highlights the need for further research that can lead to a better understanding of human–virtual human interaction.

Distance Compression in the HTC Vive Pro: A Quick Revisitation of Resolution

Spatial perception in immersive virtual environments, particularly regarding distance perception, is a well-studied topic in virtual reality literature. Distance compression, or the underestimation of distances, is and has been historically prevalent in all virtual reality systems. The problem of distance compression still remains open, but recent advancements have shown that as systems have developed, the level of distance compression has decreased. Here, we add evidence to this trend by beginning the assessment of distance compression in the HTC Vive Pro. To our knowledge, there are no archival results that report any findings about distance compression in this system. Using a familiar paradigm for studying distance compression in virtual reality hardware, we asked users to blind walk to a target object placed in a virtual environment and assessed their judgments based on those distances. We find that distance compression in the HTC Vive Pro mirrors that of the HTC Vive. Our results are not particularly surprising, considering the nature of the differences between the two systems, but they lend credence to the finding that resolution does not affect distance compression. More extensive study should be performed to reinforce these results.

Application of Causal Inference to the Analysis of Occupant Thermal State and Energy Behavioral Intentions in Immersive Virtual Environments

Identification and quantitative understanding of factors that influence occupant energy behavior and thermal state during the design phase are critical in supporting effective energy-efficient design. To achieve this, immersive virtual environments (IVEs) have recently shown potential as a tool to simulate occupant energy behaviors and collect context-dependent behavior data for buildings under design. On the other hand, prior models of occupant energy behaviors and thermal states used correlation-based approaches, which failed to capture the underlying causal interactions between the influencing factors and hence were unable to uncover the true causing factors. Therefore, in this study, the authors investigate the applicability of causal inference for identifying the causing factors of occupant/participant energy behavioral intentions and their thermal states in IVE condition and compare those results with the baseline in-situ condition. The energy behavioral intentions here are a proximal antecedent of actual energy behaviors. A set of experiments involving 72 human subjects were performed through the use of a head-mounted device (HMD) in a climate chamber. The subjects were exposed to three different step temperatures (cool, neutral, warm) under an IVE and a baseline in-situ condition. Participants' individual factors, behavioral factors, skin temperatures, virtual experience factors, thermal states (sensation, acceptability, comfort), and energy behavioral intentions were collected during the experiments. Structural causal models were learnt from data using the elicitation method in conjunction with the PC-Stable algorithm. The findings show that the causal inference framework is a potentially effective method for identifying causing factors of thermal states and energy behavioral intentions as well as quantifying their causal effects. In addition, the study shows that in IVE experiments, the participants' virtual experience factors such as their immersion, presence, and cybersickness were not the causing factors of thermal states and energy behavioral intentions. Furthermore, the study suggests that participants' behavioral factors such as their attitudes toward energy conservation and perceived behavioral control to conserve energy were the causing factors of their energy behavioral intentions. Also, the indoor temperature was a causing factor of general thermal sensation and overall skin temperature. The paper also discusses other findings, including discrepancies, limitations of the study, and recommendations for future studies.