Three-dimensional non-Euclidean geometries are highly abstract mathematical constructs, typically introduced only at advanced levels of mathematical education due to their conceptual and technical complexity. This paper presents a study exploring whether intuitive understanding of one such geometry – S2× E, the product of a 2-sphere and a line – can be facilitated through an interactive virtual reality (VR) experience without relying on formal mathematical instruction. We designed an immersive VR environment that allows users to explore S2× E as inhabitants of this space. Participants, who were neither mathematics experts nor students specializing in mathematics, engaged with the system after a short introduction using a learn-by-doing approach. Results show that participants were able to infer and articulate key properties of S2× E without knowledge prior to the experiment, and that their performance was independent of their educational background or attitude towards mathematics. Our findings demonstrate the potential of VR as a medium for conveying abstract geometric concepts through embodied learning, making them accessible to non-expert audiences.

International audience

Recent artistic explorations in VR environments have explored how users engage with virtual spaces, sounds, and bodies whether, as artists, coders, or spectators. While many applications focus primarily on musical interaction or motion capture, few allow users to actively compose spatial and temporal relationships within a multimodal collaborative creation context merging altogether different artistic modalities. In this project, we present a Virtual Reality application that enables novel forms of collaboration between dancers and musicians. The application allows dancers to record full-body performances, which musicians can then use as the basis for sonic composition by spatially mapping sound triggers onto the dancer’s recorded movement. Rather than relying on live capture or real-time gesture tracking, our approach treats movement as a timeline for interaction blending choreography with sound design in an asynchronous workflow. We employ an iterative design process to ensure usability among experts. This paper details the first implementation and study involving 10 participants recruited from professional and amateur artists with electronic music backgrounds, highlighting positive reception of the application’s creative potential and usability.

Training machine learning models to identify human behavior is a difficult yet essential task to develop autonomous and adaptive systems such as smart homes. These models require large and diversified amounts of labeled data to be trained effectively. Due to the high variety of home environments and occupant behaviors, collecting datasets that are representative of all possible homes is a major challenge. In addition, privacy and cost are major hurdles to collect real home data. To avoid these difficulties, one solution consists of training these models using purely synthetic data, which can be generated through the simulation of home and their occupants. Two challenges arise from this approach: designing a methodology with a simulation able to generate credible simulated data and evaluating this credibility. In this article, we explain the methodology used to generate diversified synthetic data of daily activities, through the combination of an agent model to simulate an occupant and a simulated 3D house enriched with sensors and effectors to produce such data. We demonstrate the credibility of the generated synthetic data by comparing their efficacy for training human context understanding models against the efficacy generated by real data. To achieve this, we replicate a real dataset collection setting with our smart home simulator. The occupant is replaced by an autonomous agent following the same experimental protocol used for the real dataset collection. This agent is a BDI-based model enhanced with a scheduler designed to offer a balance between control and autonomy. This balance is useful in synthetic data generation since strong constraints can be imposed on the agent to simulate desired situations while allowing autonomous behaviors outside these constraints to generate diversified data. In our case, the constraints are those imposed during the real dataset collection that we want to replicate. The simulated sensors and effectors were configured to react to the agent’s behaviors similarly to the real ones. We experimentally show that data generated from this simulation are valuable for two human context understanding tasks: current human activity recognition and future human activity prediction. In particular, we show that models trained solely with simulated data can give reasonable predictions about real situations occurring in the original dataset. We also report experimental results regarding statistical analysis and C2ST to assess the credibility of generated data. We discuss the generality of our approach for evaluating the credibility of simulated data from their use as training data.

In the case of non-holonomic robot navigation, path planning algorithms such as Rapidly-exploring Random Tree (RRT) rarely provide feasible and smooth paths without the need of additional processing. Furthermore, in a transport context like power wheelchair navigation, passenger comfort should be a priority and influence path planning strategy. In this paper, we propose a local path planner which guarantees bounded curvature value and continuous Cubic Bézier piecewise curves connection. To simulate and test this Cubic Bézier local path planner, we developed a new RRT version (CBB-RRT*) which generates on-the fly comfortable path adapted to non-holonomic constraints.

Power wheelchairs are one of the main solutions for people with reduced mobility to maintain or regain autonomy and a comfortable and fulfilling life. However, driving a power wheelchair in a safe way is a difficult task that often requires training methods based on real-life situations. Although these methods are widely used in occupational therapy, they are often too complex to implement and unsuitable for some people with major difficulties. In this context, we collaborated with clinicians to develop a Virtual Reality based power wheelchair simulator. This simulator is an innovative training tool adapted to any type of situations and impairments. In this paper, we present a clinical study in which 29 power wheelchair regular users were asked to complete a clinically validated task designed by clinicians within two conditions: driving in a virtual environment with our simulator and driving in real conditions with a real power wheelchair. The objective of this study is to compare performances between the two conditions and to evaluate the Quality of Experience provided by our simulator in terms of Sense of Presence and Cybersickness. Results show that participants complete the tasks in a similar amount of time for both real and virtual conditions, using respectively a real power wheelchair and our simulator. Results also show that our simulator provides a high level of Sense of Presence and provokes only slight to moderate Cybersickness discomforts resulting in a valuable Quality of Experience.

Autonomy and the ability to maintain social activities can be challenging for people with disabilities experiencing reduced mobility. In the case of disabilities that impact mobility, power wheelchairs can help such people retain or regain autonomy. Nonetheless, driving a power wheelchair is a complex task that requires a combination of cognitive, visual and visuo-spatial abilities. In practice, people need to pass prior ability tests and driving training before being prescribed a power wheelchair by their therapist. Still, conventional training in occupational therapy can be insufficient for some people with severe cognitive and/or visuo-spatial functions. As such, these people are often prevented from obtaining a power wheelchair prescription from their therapist due to safety concerns. In this context, driving simulators might be efficient and promising tools to provide alternative, adaptive, flexible, and safe training. In previous work, we proposed a Virtual Reality (VR) driving simulator integrating vestibular feedback to simulate wheelchair motion sensations. The performance and acceptability of a VR simulator rely on satisfying user Quality of Experience (QoE). Therefore, our simulator is designed to give the user a high Sense of Presence (SoP) and low Cybersickness. This paper presents a pilot study assessing the impact of the vestibular feedback provied on user QoE. Participants were asked to perform a driving task whilst in the simulator under two conditions: with and without vestibular feedback. User QoE is assessed through subjective questionnaires measuring user SoP and cybersickness. The results show that vestibular feedback activation increases SoP and decreases cybersickness. This study constitutes a mandatory step before clinical trials and, as such, only enrolled people without disabilities.

Due to the nature of Virtual Reality (VR) research, conducting experiments in order to validate the researcher's hypotheses is a must. However, the development of such experiments is a tedious and time-consuming task. In this work, we propose to make this task easier, more intuitive and faster with a method able to describe and generate the most tedious components of VR experiments. The main objective is to let experiment designers focus on their core tasks: designing, conducting, and reporting experiments. To that end, we propose the use of Domain-Specific Languages (DSLs) to ease the description and generation of VR experiments. An analysis of published VR experiments is used to identify the main properties that characterize VR experiments. This allowed us to design AGENT (Automatic Generation of ExperimeNtal proTocol runtime), a DSL for specifying and generating experimental protocol runtimes. We demonstrated the feasibility of our approach by using AGENT on two experiments published in the VRST'16 proceedings.

This paper presents the use of Virtual Reality (VR) for movie actors rehearsal of VFX-enhanced scenes. The impediment behind VFX scenes is that actors must be filmed in front of monochromatic green or blue screens with hardly any cue to the digital scenery that is supposed to surround them. The problem is worsens when the scene includes interaction with digital partners. The actors must pretend they are sharing the set with imaginary creatures when they are, in fact, on their own on an empty set. To support actors in this complicated task, we introduce the use of VR for acting rehearsals not only to immerse actors in the digital scenery but to provide them with advanced features for rehearsing their play. Indeed, our approach combines a fully interactive environment with a dynamic scenario feature to allow actors to become familiar with the virtual elements while rehearsing dialogue and action at their own speed. The interactive and creative rehearsals enabled by the system can be either single-user or multi-user. Moreover, thanks to the wide range of supported platforms, VR rehearsals can take place either on-set or off-set. We conducted a preliminary study to assess whether VR training can replace classical training. The results show that VR-trained actors deliver a performance just as good as ordinarily trained actors. Moreover, all the subjects in our experiment preferred VR training to classic training.

The main objective of this paper is to study and formalize the Take-Over Control in Collaborative Virtual Environments for Training (CVET). The Take-Over Control represents the transfer (the take over) of the interaction control of an object between two or more users. This paradigm is particularly useful for training scenarios, in which the interaction control could be continuously exchanged between the trainee and the trainer, e.g. the latter guiding and correcting the trainee's actions. The paper presents the formalization of the Take-Over Control followed by an illustration focusing in a use-case of collaborative maritime navigation. In the presented use-case, the trainee has to avoid an under-water obstacle with the help of a trainer who has additional information about the obstacle. The use-case allows to highlight the different elements a Take-Over Control situation should enforce, such as user's awareness. Different Take-Over Control techniques were provided and evaluated focusing on the transfer exchange mechanism and the visual feedback. The results show that participants preferred the Take-Over Control technique which maximized the user awareness.

We propose a method to perform collision detection with cloths with ray-tracing. Our method is able to perform collision detection between cloths and volumetric objects (rigid or deformable) as well as collision detection between cloths (including auto-collision). Our method casts rays between objects to perform collision detection, and an inversion-handling algorithm is introduced to correct errors introduced by discrete simulations. GPU computing is used to improve the performances. Our implementation handles scenes containing deformable objects at an interactive frame-rate, with collision detection lasting a few milliseconds.

Summary form only given. The CORVETTE project aims at producing significant innovations in the field of collaborative virtual training. For that purpose, CORVETTE combines various technologies to enhance the effective collaboration between users and virtual humans performing a common task. First, CORVETTE proposes a model of collaborative interaction in virtual environments allowing actors to efficiently collaborate as a team whether they are controlled by a user or by a virtual human [4]. Moreover, the environment is simulated in real-time, at real scale and is using physics as well as physically-based humanoids to improve the realism of the training. Second, thanks to the interaction model, we defined a protocol of exchange of avatars [5, 3, 2]. Thus, an actor can dynamically exchange the control of his/her avatar with the one controlled by another user or by a virtual agent. Moreover, to improve the exchange protocol, we designed a new knowledge model embedded in each avatar. It allows users and virtual humans to retrieve knowledge previously gathered by an avatar following an exchange. The preservation of knowledge is indeed especially crucial for teamwork. Finally, we handle verbal communication between users and virtual humans with speech recognition and synthesis. Actors' knowledge is enhanced through dialogue and used for decision-making and conversation [1].

We describe an experiment whose goal is to investigate the usage of different audio rendering techniques delivered through headphones while walking inside a wide four-side CAVE environment. In our experiment, participants had to physically walked along a virtual path exposed to different auditory stimuli. Each subject was exposed to three conditions: Stereo, Binaural sound spatially congruent with visual and binaural sound spatially incongruent with visuals and had to rate subjectively each. The results of the experiment showed increased preference ratings for the binaural audio rendering, followed by stereo rendering. As expected incongruent spatial cues were ranked significantly lower. Binaural rendering can deliver an increased immersive experience and do no require specialized hardware.

In four-sided CAVE-like VR systems, the absence of the rear wall has been shown to decrease the level of immersion and can introduce breaks in presence. With this experiment we analyse how user attention is diverted while physically walking in a virtual environment, when audio and/or visual attractors are present. Key features of the experiment are the fact that auditory feedback was delivered through binaural audio rendering dependent on the user's head position and orientation and that the four-sided CAVE used for the experiment allowed users to walk up to 9m in straight line. In the experiment we analysed how different “attractors” (audio and/or visual, static or dynamic) modify the user's attention. Results of the experiment show that audio-visual attractors are the most efficient attractors in order to keep the user's attention toward the inside of the CAVE.

ABSTRACT Virtual agents are a real asset in collaborative virtual environment for training (CVET) as they can replace missing team members. Collaboration between such agents and users, however, is generally limited. We present here a whole integrated model of CVET focusing on the abstraction of the real or virtual nature of the actor to define a homogenous collaboration model. First, we define a new collaborative model of interaction. This model notably allows to abstract the real or virtual nature of a teammate. Moreover, we propose a new role exchange approach so that actors can swap their roles during training. The model also permits the use of physically based objects and characters animation to increase the realism of the world. Second, we design a new communicative agent model, which aims at improving collaboration with other actors using dialog to coordinate their actions and to share their knowledge. Finally, we evaluated the proposed model to estimate the resulting benefits for the users and we show that this is integrated in existing CVET applications. Copyright © 2014 John Wiley & Sons, Ltd.

The exchange of avatars, i.e. the actual fact of changing once avatar with another one, is a promising trend in multi-actor virtual environments. It provides new opportunities for users, such as controlling a different avatar for a specific action, retrieving knowledge belonging to a particular avatar, solving conflicts and deadlocks situations or even helping another user. Virtual Environments for Training are especially affected by this trend as a specific role derived from a scenario is usually assigned to a unique avatar. Despite the increasing use of avatar exchange, users' perception and understanding of this mechanism have not been studied. In this paper, we propose two complementary user-centered evaluations that aim at comparing several representations for the exchange of avatars; these are termed exchange metaphors. Our first experiment focuses on the perception of an exchange by a user who is not involved in the exchange, and the second experiment analyzes the perception of an exchange triggered by the user. Results show that the use of visual feedback globally aids better understanding of the exchange mechanism in both cases. Our first experiment suggests, however, that visual feedback is less efficient than a simple popup notification in terms of task duration. In addition, the second experiment shows that much simpler metaphors with no visual effect are generally preferred because of their efficiency.

We present IRTCD, a novel Iterative Ray-Traced Collision Detection algorithm that exploits spatial and temporal coherency. Our approach uses any existing standard ray-tracing algorithm and we propose an iterative algorithm that updates the previous time step results at a lower cost with some approximations. Applied for rigid bodies, our iterative algorithm accelerate the collision detection by a speedup up to 33 times compared to non-iterative algorithms on GPU.

In the context of scientific data analysis, we propose to compare a remote collaborative manipulation technique with a single user manipulation technique. The manipulation task consists in positioning a clipping plane in order to perform cross-sections of scientific data that show several points of interest located inside these data. For the remote collaborative manipulation, we have chosen to use the 3-hand manipulation technique proposed by Aguerreche et al., which is very suitable with a remote manipulation of a plane. We ran two experiments to compare the two manipulation techniques with some participants located in two different countries. These experiments has shown that the remote collaborative manipulation technique was significantly more efficient than the single user manipulation when the 3 points of interest were far apart inside the scientific data and, consequently, when the manipulation task was more difficult and required more precision. When the 3 points of interest were close together, there was not significant difference between the two manipulation techniques.

We describe a parallel framework for interactive smooth rendering of massive terrains. We define a parallelization scheme for level of detail algorithms in cluster-based environments. The scheme relies on modern PC clusters capabilities to address the scalability issue of level of detail algorithms. To achieve this, we propose an eficient tile-based data partitioning method that allows both reducing load imbalance and solving the well-known border problem. At runtime level of detail computations are performed in parallel on cluster nodes. A hierarchical view frustum culling combined to a compression mechanism harnessing the frame-to-frame coherence are used to drastically reduce the inter-tasks communication overhead. We take into account level of detail algorithms visual quality issue by providing geomorphing and texturing supports. We are able to interactively and smoothly render terrains composed of hundreds of millions to billions of polygons on a cluster of 8 PCs.

Net Juggler is an open source library that turns a commodity component cluster running the VR Juggler platform on each node into a single VR Juggler image cluster. Application parallelization is transparent to the user and leads to high performance executions even with limited bandwidth networks.

Slicing analyses have been proposed for different programming languages. Rather than defining a new analysis from scratch for each programming language, we would like to specify such an analysis once and for all, in a language-independent way, and then specialize it for different programming languages. In order to achieve this goal, we propose a notion of natural semantics format and a dynamic slicing analysis format. The natural semantics format formalizes a class of natural semantics and the analysis format is a generic, language-independent, slicing analysis. The correctness of the generic analysis is established as a relation between the derivation trees of the original program and the slice. This generic analysis is then instantiated to several programming languages conforming to the semantics format (an imperative language, a logic programming language and a functional language), yielding a dynamic slicing analyzer for each of these languages.