Anne-Hélène Olivier

Virtual Reality (VR) provides a powerful framework for studying human social interactions, yet generating realistic and diverse interaction data remains a major challenge. Asynchronous Interaction (AsI) paradigms offer a promising alternative by enabling users to interact with their own previously recorded behaviors. However, their ability to reproduce fine-grained social mechanisms is still unclear. To investigate whether AsI preserves proxemic behaviors observed in dyadic interactions, we carried out two user studies. The first study leverages AsI and VR to capture social dyadic interactions, where participants alternated between approaching a virtual human and being approached by a virtual human animated from their prior recordings. We analyze AsI through four key factors: gender, approach orientation, movement velocity, and attractiveness. Results show that asynchronous interactions reproduce several established proxemic effects: women maintain larger distances and exhibit greater reactive displacements, frontal approaches elicit stronger avoidance than lateral ones, and velocity modulates both displacement and gender effects. Moreover, movements and agents perceived as less attractive tend to elicit stronger reactions. In the second study, we assessed the perceived realism, behavior, awareness, and appropriateness of interaction distances using videos of both asynchronous and synchronous interactions (the latter created through a traditional synchronous approach). Our findings support the fact that AsI preserves several measurable proxemic regularities and confirm the use of AsI as a valid and scalable framework for studying social behavior in VR and for generating interaction datasets. Finally, we provide guidelines for designing socially aware virtual agents.

This article deals with the experimental study of pedestrian behaviors in some situations of one-dimensional traffic. Participants were preorganized in a line and asked to walk either in a straight line with a fast or slow leader or to form a circle. The originality of our experimental protocol compared to previous ones on similar situations is to have left the condition of density free. While the observed density results from individual decisions in the line case, both density and velocity have to be collectively chosen in the case of circle formation. Our major findings are the following. In the case of circle formations, we observe that the resulting velocity is very stable among realizations, as if collective decision was playing the role of an average. For line flows with a slow leader, the same operating point close to the jamming transition is chosen as in previous experiments where it was not velocity but density that was imposed. In the less constrained line experiments, although participants could choose comfortable headways, they rather stuck to short headways requiring a faster adaption-a fact that could come from a "social pressure from behind" and had previously been observed only at high densities. This could be due to the uncertainty and individual responsibility that stem in less constrained situations. Our results open avenues for future research, as they show that the walking values preferred by humans in following tasks depend on more factors than previously considered.

Virtual human animations have a wide range of applications in virtual and augmented reality. While automatic generation methods of animated virtual humans have been developed, assessing their quality remains challenging. Recently, approaches introducing task-oriented evaluation metrics have been proposed, leveraging neural network training. However, quality assessment measures for animated virtual humans not generated with parametric body models have yet to be developed. In this context, we introduce a first such quality assessment measure leveraging a novel data-driven framework. First, we generate a dataset of virtual human animations together with their corresponding subjective realism evaluation scores collected with a user study. Second, we use the resulting dataset to learn predicting perceptual evaluation scores. Results indicate that training a linear regressor on our dataset results in a correlation of $90\%$, which outperforms a strong deep learning baseline.

Immersive environments provide opportunities to learn and transfer skills to real life. This opens up new areas of application, such as rehabilitation, where people with neurological disabilities can learn to drive a power wheelchair (PWC) through the development of immersive simulators. To expose these specific users to daily-life study interaction situations, it is important to ensure realistic interactions with the virtual humans that populate the simulated environment, as PWC users should learn to drive and navigate under everyday conditions. While non-verbal pedestrian-pedestrian interactions have been extensively studied, understanding pedestrian-PWC user interactions during locomotion is still an open research area. Our study aimed to investigate the regulation of interpersonal distance (i.e., proxemics) between a pedestrian and a PWC user in real and virtual situations. We designed 2 experiments in which 1) participants had to reach a goal by walking (respectively driving a PWC) and avoid a static PWC confederate (respectively a standing confederate) and 2) participants had to walk to a goal and avoid a static confederate seated on a PWC in real and virtual conditions. Our results showed that interpersonal distances were significantly different whether the pedestrian avoided the PWC user or vice versa. We also showed an influence of the orientation of the person to be avoided. We discuss these findings with respect to pedestrian-pedestrian interactions, as well as their implications for the design of virtual humans interacting with PWC users for rehabilitation applications. In particular, we proposed a proof of concept by adapting existing microscopic crowd simulation algorithms to consider the specificity of pedestrian-PWC user interactions.

We propose a novel contact-aware method to synthesize highly-dense 3D crowds of animated characters. Existing methods animate crowds by, first, computing the 2D global motion approximating subjects as 2D particles and, then, introducing individual character motions without considering their surroundings. This creates the illusion of a 3D crowd, but, with density, characters frequently intersect each other since character-to-character contact is not modeled. We tackle this issue and propose a general method that considers any crowd animation and resolves existing residual collisions. To this end, we take a physics-based approach to model contacts between articulated characters. This enables the real-time synthesis of 3D high-density crowds with dozens of individuals that do not intersect each other, producing an unprecedented level of physical correctness in animations. Under the hood, we model each individual using a parametric human body incorporating a set of 3D proxies to approximate their volume. We then build a large system of articulated rigid bodies, and use an efficient physics-based approach to solve for individual body poses that do not collide with each other while maintaining the overall motion of the crowd. We first validate our approach objectively and quantitatively. We then explore relations between physical correctness and perceived realism based on an extensive user study that evaluates the relevance of solving contacts in dense crowds. Results demonstrate that our approach outperforms existing methods for crowd animation in terms of geometric accuracy and overall realism.

This work introduces PERCEPT-PIETON, a project aiming to understand through the use of virtual reality and inertial motion capture, perceptual-motor and cognitive mechanisms involved during driver-pedestrian interactions from the driver’s point of view. We are be particularly interested in how drivers regulate their behaviour and predict pedestrian’s intentions based on individual (age, gender,...) and behavioural (walking speed, trajectory,...) characteristics of the latter. The objective is threefold : (i) a theoretical scientific contribution to obstacle avoidance literature; (ii) applied scientific knowledge for public decision-makers; and (iii) the creation of a free access library of biological pedestrians movements.