Non-player characters (NPCs) are a vital part of many 3D and virtual reality (VR) experiences, but animating them can be a time-consuming and costly process, often requiring expensive motion-capture setups, especially when tracking multiple people simultaneously. Single-user record and replay in VR enables a single user to animate multiple NPCs on their own, offering a cheaper and more convenient solution as the devices are consumer-grade and already track user movements. A limitation of single-user record and replay is that an early recorded character has no information about a later recorded character and might not be able to interact with them believably. We therefore study interactivity between NPCs as an emergent property when actors can see other characters in later recordings as opposed to acting primarily on their own in the first recording. We conducted a user study (N=144) where participants compared first, second, and third recording runs of different social scenarios with multiple NPCs. Our results suggest that for scenarios with low and medium interactivity where timing is not crucial and characters are not in close contact, a single run can be sufficient, whereas for high-interactivity scenarios, with close-contact group interactions, two recordings are more appropriate.

We compared the body movements of five virtual reality (VR) avatar representations in a user study $(\mathrm{N}=53)$ to ascertain how well these representations could convey body motions associated with different emotions: one head-and-hands representation using only tracking data, one upper-body representation using inverse kinematics (IK), and three full-body representations using IK, motioncapture, and the state-of-the-art deep-learning model AGRoL. Participants’ emotion detection accuracies were similar for the IK and AGRoL representations, highest for the full-body motion-capture representation and lowest for the head-and-hands representation. Our findings suggest that from the perspective of emotion expressivity, connected upper-body parts that provide visual continuity improve clarity, and that current techniques for algorithmically animating the lower-body are ineffective. In particular, the deep-learning technique studied did not produce more expressive results, suggesting the need for training data specifically made for social VR applications.

Social Virtual Reality (SVR) systems are growing in both popularity and breadth. New systems are attempting to support a wider range of increasingly large social situations. A key characteristic of SVR systems is user capacity. Many systems are designed for small group discussion or team games, and support up to 20-30 users. Some support larger counts, but via special features coupled to a specific social situation. As SVR broaches medium to large gatherings (10s-1000s of people), capacity becomes a major challenge. Naïve implementations where each user communicates directly with every other will quickly overwhelm the resources of the system. The constraints on scalability have been known since the first SVR systems were created. However, while there are many systems out there, their scalability mechanisms are often application specific, and rarely openly discussed. In this paper we explore scalability in the open-source SVR Ubiq. We consider two partitioning schemes to increase relay-server capacity. We examine the current failure modes of the relay-server system, and demonstrate how these schemes improve capacity by a factor of 2-3x. We look at the interactions between the schemes and simulated user behaviour, to see what lessons can be gleaned for research into scalability for SVR.

Mixed-reality systems provide a number of different ways of representing users to each other in collaborative scenarios. There is an obvious tension between using media such as video for remote users compared to representations as avatars. This paper includes two experiments (total n = 80) on user trust when exposed to two of three different user representations in an immersive virtual reality environment that also acts as a simulation of typical augmented reality simulations: full body video, head and shoulder video and an animated 3D model. These representations acted as advisors in a trivia quiz. By evaluating trust through advisor selection and self-report, we found only minor differences between representations, but a strong effect of perceived advisor expertise. Unlike prior work, we did not find the 3D model scored poorly on trust, perhaps as a result of greater congruence within an immersive context.

We present Monte-Carlo Redirected Walking (MCRDW), a gain selection algorithm for redirected walking. MCRDW applies the Monte-Carlo method to redirected walking by simulating a large number of simple virtual walks, then inversely applying redirection to the virtual paths. Different gain levels and directions are applied, producing differing physical paths. Each physical path is scored and the results used to select the best gain level and direction. We provide a simple example implementation and a simulation-based study for validation. In our study, when compared with the next best technique, MCRDW reduced incidence of boundary collisions by over 50% while reducing total rotation and position gain.

While they have long been a subject of academic study, social virtual reality (SVR) systems are now attracting increasingly large audiences on current consumer virtual reality systems. The design space of SVR systems is very large, and relatively little is known about how these systems should be constructed in order to be usable and efficient. In this paper we present Ubiq, a toolkit that focuses on facilitating the construction of SVR systems. We argue for the design strategy of Ubiq and its scope. Ubiq is built on the Unity platform. It provides core functionality of many SVR systems such as connection management, voice, avatars, etc. However, its design remains easy to extend. We demonstrate examples built on Ubiq and how it has been successfully used in classroom teaching. Ubiq is open source (Apache License) and thus enables several use cases that commercial systems cannot.

Redirected walking allows for natural locomotion in virtual environments that are larger than a user’s physical environment. The mapping between real and virtual motion is modified by scaling some aspect of motion. As a user traverses the virtual environment these modifications (or gains) must be dynamically adjusted to prevent collision with physical obstacles. A significant body of work has established perceptual thresholds on rates of absolute gain, but the effect of changing gain is little understood.

In virtual reality a real walking interface limits the extent of a virtual environment to our local walkable space. As local spaces are specific to each user, sharing a virtual environment with others for collaborative work or games becomes complicated. It is not clear which user's walkable space to prefer, or whether that space will be navigable for both users.

This paper presents the design and implementation of Geosphere, a physical- and link-layer design for access point-based MIMO wireless networks that consistently improves network throughput. To send multiple streams of data in a MIMO system, prior designs rely on a technique called zero-forcing, a way of "nulling" the interference between data streams by mathematically inverting the wireless channel matrix. In general, zero-forcing is highly effective, significantly improving throughput. But in certain physical situations, the MIMO channel matrix can become "poorly conditioned," harming performance. With these situations in mind, Geosphere uses sphere decoding, a more computationally demanding technique that can achieve higher throughput in such channels. To overcome the sphere decoder's computational complexity when sending dense wireless constellations at a high rate, Geosphere introduces search and pruning techniques that incorporate novel geometric reasoning about the wireless constellation. These techniques reduce computational complexity of 256-QAM systems by almost one order of magnitude, bringing computational demands in line with current 16- and 64-QAM systems already realized in ASIC. Geosphere thus makes the sphere decoder practical for the first time in a 4 × 4 MIMO, 256-QAM system. Results from our WARP testbed show that Geosphere achieves throughput gains over multi-user MIMO of 2× in 4 × 4 systems and 47% in 2 × 2 MIMO systems.