Julien Pettré

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.

Our goal in this article is to propose a fast and easy to implement BRDF modeling method that provides both accurate and compact representations for all types of BRDF, i.e., isotropic or anisotropic. To achieve this objective, we use a Bayesian regression method with a Gaussian process prior which allows obtaining compact BRDF representations in a purely analytical way. For this purpose, we use a generalzed distance covariance kernel which is much better suited to BRDF features than the usual Gaussian kernel. To speed up the processing, we adapt this method to the specificities of BRDFs through an appropriate input data structure and distribution of observations so as to drastically reduce the problem dimensionality through an efficient factorization method. In this way, all calculations at both fitting and rendering steps are reduced to basic matrix products and the computation of a BRDF representation with our modeling method takes only a few seconds. Furthermore, rather than using a systematic approach as in state-of-the-art methods, the size and complexity of the BRDF representation can be adapted to the application requirements as regards the fitting accuracy and rendering constraints. Besides, our BRDF representation can be easily converted to spherical harmonics expansions, which allows easier integration in usual rendering algorithms. We also propose importance sampling methods derived from our BRDF modeling method that leads to fast and easy implementations. Experimental applications of our method to various types of isotropic and anisotropic BRDFs show that state-of-the-art methods can be outperformed in most cases by using a small set of observations for the regression.

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.

Crowd authoring has mainly focused on generalised agent interactions such as collision avoidance and grouping. However, in society, people interact more intentionally with specific “sources” such as exhibits, or inspectors. Uncovering these interactions is essential for understanding and characterising social behaviours. We propose a model that learns from trajectories, the localised agent interactions imposed by the context of the object or agent source. Our model decomposes agent paths into sequential combinations of simple and understandable “core” behaviours, like approach, stop, and circle around, temporally dissecting source-centric trajectories into standardised movements. We train on pairs of trajectory-encoded images and their associated core behaviour combination. Given a set of trajectories around a specific source, our framework can be applied to build a behaviour distribution, summarising how people interact with the source type. The inferred distribution can then be sampled to generate diverse crowds of context-aware agents. We evaluate our model using collected ground-truth data and perform a case study that showcases the utility of this decomposition of context-aware interactions in other tasks, such as measuring behaviour similarity.

This paper proposes a projection system that optically removes the cast shadow in projection mapping. Specifically, we realize the large-aperture (LA) projection using a large-format Fresnel lens to suppress cast shadows by condensing the projection light from a wide viewing angle. However, the resolution and contrast of the projected results are significantly degraded by defocus blur, veiling glare, and stray light caused by the aberration of an LA Fresnel lens. To solve the technical problems, we employ two different approaches: optical and digital image processing methods. First, we introduce a residual projector with a typical aperture lens on the same optical axis as the LA projector, projecting the residual (i.e., high-frequency) components attenuated in the LA projection. These projectors play different roles in shadow suppression and blur compensation, both achieved by projecting simultaneously. Secondly, we optimize the pair of projection images that can balance the shadow suppression and deblurring performance of our projection system. We implemented a proof-of-concept prototype and validated the above-mentioned techniques through projection experiments and a user study.

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.

Avoiding moving obstacles in immersive environments requires adjustments in the walking trajectory and depends on the type of obstacle movement. Previous research studied the impact of speed and direction of motion but not much is known about how predictability of motion impacts human circumvention in terms of distance to the obstacle (proximity). In this paper, we investigate how participants navigate through VR collision avoidance scenarios with obstacles of varying motion characteristics in terms of speed and predictability. We introduce a novel concept of creating unpredictable motion using entropy calculations. We anticipated that higher entropy would increase the proximity distance which we measured with several metrics related to the distance from the obstacle and centre of the scene. We found a significant influence of motion speed and predictability on proximity-related metrics, with participants exhibiting a tendency to maintain larger distances in scenarios where obstacle speed and entropy were higher. We also outline two decision-making strategies for avoidance behaviour and investigate the factors that influence individuals’ selection of one strategy over the other.