Many VR animal embodiment sytsems suffer from poor animation fidelity, typically animating the animal avatars using inverse kinematics. We address this issue, presenting a novel deep-learning method, centred around a shared codebook, for mapping human motion to quadruped motion. Rather than trying to directly bridge the gap from human motion to quadruped motion, a task which has proven difficult, we first use a rule-based retargeter, relying on inverse and forward kinematics, to retarget human motions to an intermediate motion domain in which the motions share the same skeleton as the quadruped. We then use finite scalar quantization to construct a shared latent space, or codebook, between this intermediate domain and the quadruped motion domain. We do this by first pre-defining a finite number of discrete latent codes and then teaching these codes, using unsupervised deep-learning, to represent semantically similar motions in the two domains. We incorporate our real-time human-to-quadruped motion mapping into a VR quadruped embodiment system. The output quadruped animations are natural and realistic, while also preserving the semantics of users’ actions. Moreover, there is a strong synchrony between the input human motions and retargeted quadruped motions, an important factor for inducing a strong sense of VR embodiment.

Nonverbal communication is paramount in daily life, as well as in populated virtual reality (VR) environments. In this paper, we focused on gaze behaviour, which is key to initiate and drive social interactions. Previous work on photographs and on virtual agents showed the importance of gaze, even in the presence of multiple stimuli, by demonstrating the stare-in-the-crowd effect: humans detect faster and observe gazes directed towards them longer than the averted ones. While previous studies focused on static scenarios, which fail in representing the complexity of real-life social interactions, we propose to explore the stare-in-the-crowd effect in dynamic situations. To this end, we designed a within-subject experiment where 21 users navigated a virtual street through an idle or moving crowd of virtual agents. Agents’ gaze was manipulated to display averted, directed, or shifting gaze. We analysed the user’s gaze (fixations, dwell time) and locomotor behaviours (path decisions, proximity to agents) as well as their social anxiety. Results showed that the stare-in-the-crowd effect is preserved when walking through both types of crowd, and that social anxiety decreases gaze interaction time and affects proximity behaviours in case of agents with directed gazes. However, virtual agents’ gaze did not elicit significant changes on users’ locomotion. These findings highlight the importance of considering virtual agents’ gaze when creating VR environments, and open future work perspectives to better understand factors that would strengthen or decrease this effect at gaze and locomotor levels.

Nonverbal cues are paramount in real-world interactions. Among these cues, gaze has received much attention in the literature. In particular, previous work has shown a search asymmetry between directed and averted gaze towards the observer using photographic stimuli, with faster detection and longer fixation towards directed gaze by the observer. This is known as the stare-in-the-crowd effect. In this study, we investigate whether stare-in-the crowd effect is preserved in Virtual Reality (VR). To this end, we designed a within-subject experiment where 30 human users were immersed in a virtual environment in front of an audience of 11 virtual agents following 4 different gaze behaviours. We analysed the user’s gaze behaviour when observing the audience, computing fixations and dwell time. We also collected the users’ social anxiety score using a post-experiment questionnaire to control for some potential influencing factors. Results show that the stare-in-the-crowd effect is preserved in VR, as demonstrated by the significant differences between gaze behaviours, similarly to what was found in previous studies using photographic stimuli. Additionally, we found a negative correlation between dwell time towards directed gazes and users’ social anxiety scores. Such results are encouraging for the development of expressive and reactive virtual humans, which can be animated to express natural interactive behaviour.

Virtual reality (VR) is a valuable experimental tool for studying human movement, including the analysis of interactions during locomotion tasks for developing crowd simulation algorithms. However, these studies are generally limited to distant interactions in crowds, due to the difficulty of rendering realistic sensations of collisions in VR. In this article, we explore the use of wearable haptics to render contacts during virtual crowd navigation. We focus on the behavioral changes occurring with or without haptic rendering during a navigation task in a dense crowd, as well as on potential after-effects introduced by the use haptic rendering. Our objective is to provide recommendations for designing VR setup to study crowd navigation behavior. To the end, we designed an experiment (N=23) where participants navigated in a crowded virtual train station without, then with, and then again without haptic feedback of their collisions with virtual characters. Results show that providing haptic feedback improved the overall realism of the interaction, as participants more actively avoided collisions. We also noticed a significant after-effect in the users' behavior when haptic rendering was once again disabled in the third part of the experiment. Nonetheless, haptic feedback did not have any significant impact on the users' sense of presence and embodiment.

Non-verbal communication body cues are paramount to interact. In this preliminary work, we explore ways to let Intelligent Virtual Agents (IVAs) simulating nonverbal communication capabilities. We propose an approach to control IVAs' reactive behaviour from the analysis of other agents' apparent motions, in a situation of "observed" IVAs that act and "observers" that react. For that, first a viewpoint-driven analysis of the observed agent's motion is done, and then a synthesis of this analysis induces the observers' reaction.

Placing and moving virtual cameras in real-time 3D environments is a task that remains complex due to the many requirements which need to be satisfied simultaneously. Beyond the essential features of ensuring visibility and frame composition for one or multiple targets, an ideal camera system should provide designers with tools to create variations in camera placement and motions, and create shots which conform to aesthetic recommendations. In this paper, we propose a controllable process that will assist developers and artists in placing cinematographic cameras and camera paths throughout complex virtual environments, a task that was often manually performed until now. With no specification and no previous knowledge on the events, our tool exploits a topological analysis of the environment to capture the potential movements of the agents, highlight linearities and create an abstract skeletal representation of the environment. This representation is then exploited to automatically generate potentially relevant camera positions and trajectories organized in a graph representation with visibility information. At run-time, the system can then efficiently select appropriate cameras and trajectories according to artistic recommendations. We demonstrate the features of the proposed system with realistic game-like environments, highlighting the capacity to analyze a complex environment, generate relevant camera positions and camera tracks, and run efficiently with a range of different camera behaviours.

To simulate the low-level (‘microscopic’) behavior of human crowds, a local navigation algorithm computes how a single person (‘agent’) should move based on its surroundings. Many algorithms for this purpose have been proposed, each using different principles and implementation details that are difficult to compare.