Studying pedestrian navigation in crowded environments is key to many applications, like crowd simulation for urban planning, but is challenging under real-world conditions due to the lack of experimental control. This motivates the use of virtual reality to manipulate variables and recreate complex social interactions. While research has mostly emphasized visual feedback, and eventually haptic rendering of collision, the integration of auditory cues in VR platforms, such as footstep sounds, remains overlooked. The present study investigates the role of adding footstep sounds in a VR collision-avoidance task. Twenty participants completed a within-subject experiment where a virtual pedestrian, initially visually occluded, crossed their path either with or without audible footsteps. Locomotor adjustments and gaze behavior were analyzed before and during the visual interaction. Results showed that footstep sounds induced earlier gaze orientation and more proactive trajectory adjustments from the participants, without affecting their walking speed. In contrast, the absence of sound led to more collisions and crossing order inversions, indicating reduced efficiency. These findings demonstrate that simple auditory cues can guide attention, facilitate anticipation, and improve interactions with virtual agents. Integrating sound into VR platforms is therefore essential to study pedestrian behavior and social navigation.

International audience

International audience

Populating virtual words with realistic pedestrian behaviours is challenging but essential to ensure natural interactions with users. In pedestrian encounters, successful collision avoidance requires adapting speed and/or locomotor trajectory based on situational- and personal-characteristics. Personal characteristics such as the age of a pedestrian can easily be observed, but whether it affects avoidance behaviours is unknown. This is however an important question to ensure realism and variety of the simulations. The purpose of the current study was to examine the influence of a virtual pedestrian’s (VP) age-related appearance and gait profile on avoidance behaviours during a circumvention task. We expected that older adult (OA) gait characteristics and/or appearance would result in more cautious behaviours. Young adults (YA; n=17, 23.6 ± 2.7yrs) navigated a virtual street using a HMD. Individuals walked 8m towards a goal, while avoiding an approaching VP who would approach and steer towards the participant’s left, right, or continue straight, while exhibiting different age-related appearances and gait profiles: 1. YA appearance, YA gait; 2. OA appearance, OA gait; 3. OA appearance, YA gait; and 4. YA appearance, OA gait. Results indicate that clearance was larger when the appearance of VP resembled an OA, and when the VP walked like an OA compared to a YA. Larger clearance distances observed with OA characteristics may be due to societal norms associated with the principle of parental respect as well as a cautious strategy for any potential instability (wavering) in balance commonly observed in OA. This research sheds light on how age-related cues influence pedestrian interactions, with implications for the design of populated virtual environments.

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.

Abstract Generating realistic animated virtual humans is a problem that has been extensively studied with many applications in different types of virtual environments. However, the creation process of such realistic animations is challenging, especially because of the number and variety of influencing factors, that should then be identified and evaluated. In this paper, we attempt to provide a clearer understanding of how the multiple factors that have been studied in the literature impact the level of realism of animated virtual humans, by providing a survey of studies assessing their realism. This includes a review of features that have been manipulated to increase the realism of virtual humans, as well as evaluation approaches that have been developed. As the challenges of evaluating animated virtual humans in a way that agrees with human perception are still active research problems, this survey further identifies important open problems and directions for future research.

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.

Gaze behavior of virtual characters in video games and virtual reality experiences is a key factor of realism and immersion. Indeed, gaze plays many roles when interacting with the environment; not only does it indicate what characters are looking at, but it also plays an important role in verbal and non-verbal behaviors and in making virtual characters alive. Automated computing of gaze behaviors is however a challenging problem, and to date none of the existing methods are capable of producing close-to-real results in an interactive context. We therefore propose a novel method that leverages recent advances in several distinct areas related to visual saliency, attention mechanisms, saccadic behavior modelling, and head-gaze animation techniques. Our approach articulates these advances to converge on a multi-map saliency-driven model which offers real-time realistic gaze behaviors for non-conversational characters, together with additional user-control over customizable features to compose a wide variety of results. We first evaluate the benefits of our approach through an objective evaluation that confronts our gaze simulation with ground truth data using an eye-tracking dataset specifically acquired for this purpose. We then rely on subjective evaluation to measure the level of realism of gaze animations generated by our method, in comparison with gaze animations captured from real actors. Our results show that our method generates gaze behaviors that cannot be distinguished from captured gaze animations. Overall, we believe that these results will open the way for more natural and intuitive design of realistic and coherent gaze animations for real-time applications.

Crowd data is a crucial element in the modeling of collective behaviors, and opens the way to simulation for their study or prediction. Given the difficulty of acquiring such data, virtual reality is useful for simplifying experimental processes and opening up new experimental opportunities. This comes at the cost of the need to assess the biases introduced by the use of this technology. Our paper is part of this effort, and investigates the effect of the graphical representation of a crowd on the behavior of a user immersed within. More specifically, we inspect the virtual navigation through virtual crowds, in terms of travel speeds and local navigation choices as a function of the visual representation of the virtual agents that make up the crowd (simple geometric model, anthropomorphic model or realistic model). Through an experiment in which we ask a user to navigate virtual crowds of varying densities, we show that the effect of the visual representation is limited, but that an anthropomorphic representation offers the best trade-off between computational complexity and ecological validity, even though a more realistic representation can be preferred when user behaviour is studied in more details. Our work leads to clear recommendations on the design of immersive simulations for the study of crowd behavior.

During mid-air interactions, common approaches (such as the god-object method) typically rely on visually constraining the user's avatar to avoid visual interpenetrations with the virtual environment in the absence of kinesthetic feedback. This paper explores two methods which influence how the position mismatch (positional offset) between users' real and virtual hands is recovered when releasing the contact with virtual objects. The first method (sticky) constrains the user's virtual hand until the mismatch is recovered, while the second method (unsticky) employs an adaptive offset recovery method. In the first study, we explored the effect of positional offset and of motion alteration on users' behavioral adjustments and users' perception. In a second study, we evaluated variations in the sense of embodiment and the preference between the two control laws. Overall, both methods presented similar results in terms of performance and accuracy, yet, positional offsets strongly impacted motion profiles and users' performance. Both methods also resulted in comparable levels of embodiment. Finally, participants usually expressed strong preferences toward one of the two methods, but these choices were individual-specific and did not appear to be correlated solely with characteristics external to the individuals. Taken together, these results highlight the relevance of exploring the customization of motion control algorithms for avatars.

While data is vital to better understand and model interactions within human crowds, capturing real crowd motions is extremely challenging. Virtual Reality (VR) demonstrated its potential to help, by immersing users into either simulated virtual crowds based on autonomous agents, or within motion-capture-based crowds. In the latter case, users' own captured motion can be used to progressively extend the size of the crowd, a paradigm called Record-and-Replay (2R). However, both approaches demonstrated several limitations which impact the quality of the acquired crowd data. In this paper, we propose the new concept of contextual crowds to leverage both crowd simulation and the 2R paradigm towards more consistent crowd data. We evaluate two different strategies to implement it, namely a Replace-Record-Replay (3R) paradigm where users are initially immersed into a simulated crowd whose agents are successively replaced by the user's captured-data, and a Replace-Record-Replay-Responsive (4R) paradigm where the pre-recorded agents are additionally endowed with responsive capabilities. These two paradigms are evaluated through two real-world-based scenarios replicated in VR. Our results suggest that the behaviors observed in VR users with surrounding agents from the beginning of the recording process are made much more natural, enabling 3R or 4R paradigms to improve the consistency of captured crowd datasets.

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.

Defining equivalences between poses of different human characters is an important problem for imitation research, human pose recognition and deformation transfer. However, pose equivalence is a subjective information that depends on context and on the morphology of the characters. A common hypothesis is that interactions between body surfaces, such as self-contacts, are important attributes of human poses, and are therefore consistently included in animation approaches aiming at retargeting human motions. However, some of these self-contacts are only present because of the morphology of the character and are not important to the pose, e.g. contacts between the upper arms and the torso during a standing A-pose. In this paper, we conduct a first study towards the goal of understanding the impact of self-contacts between body surfaces on perceived pose equivalences. More specifically, we focus on contacts between the arms or hands and the upper body, which are frequent in everyday human poses. We conduct a study where we present to observers two models of a character mimicking the pose of a source character, one with the same self-contacts as the source, and one with one self-contact removed, and ask observers to select which model best mimics the source pose. We show that while poses with different self-contacts are considered different by observers in most cases, this effect is stronger for self-contacts involving the hands than for those involving the arms.

Analyzing motion data is a critical step when building meaningful locomotive motion datasets. This can be done by labeling motion capture data and inspecting it, through a planned motion capture session or by carefully selecting locomotion clips from a public dataset. These analyses, however, have no clear definition of coverage, making it harder to diagnose when something goes wrong, such as a virtual character not being able to perform an action or not moving at a given speed. This issue is compounded by the large amount of information present in motion capture data, which poses a challenge when trying to interpret it. This work provides a visualization and an optimization method to streamline the process of crafting locomotive motion datasets. It provides a more grounded approach towards locomotive motion analysis by calculating different quality metrics, such as: demarcating coverage in terms of both linear and angular speeds, frame use frequency in each animation clip, deviation from the planned path, number of transitions, number of used vs. unused animations and transition cost.

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.

Abstract The real‐time simulation of human crowds has many applications. In a typical crowd simulation, each person ('agent') in the crowd moves towards a goal while adhering to local constraints. Many algorithms exist for specific local ‘steering’ tasks such as collision avoidance or group behavior. However, these do not easily extend to completely new types of behavior, such as circling around another agent or hiding behind an obstacle. They also tend to focus purely on an agent's velocity without explicitly controlling its orientation. This paper presents a novel sketch‐based method for modelling and simulating many steering behaviors for agents in a crowd. Central to this is the concept of aninteraction field(IF): a vector field that describes the velocities or orientations that agents should use around a given ‘source’ agent or obstacle. An IF can also change dynamically according to parameters, such as the walking speed of the source agent. IFs can be easily combined with other aspects of crowd simulation, such as collision avoidance. Using an implementation of IFs in a real‐time crowd simulation framework, we demonstrate the capabilities of IFs in various scenarios. This includes game‐like scenarios where the crowd responds to a user‐controlled avatar. We also present an interactive tool that computes an IF based on input sketches. This IF editor lets users intuitively and quickly design new types of behavior, without the need for programming extra behavioral rules. We thoroughly evaluate the efficacy of the IF editor through a user study, which demonstrates that our method enables non‐expert users to easily enrich any agent‐based crowd simulation with new agent interactions.

Abstract Simulating crowds requires controlling a very large number of trajectories of characters and is usually performed using crowd steering algorithms. The question of choosing the right algorithm with the right parameter values is of crucial importance given the large impact on the quality of results. In this paper, we study the performance of a number of steering policies (i.e., simulation algorithm and its parameters) in a variety of contexts, resorting to an existing quality function able to automatically evaluate simulation results. This analysis allows us to map contexts to the performance of steering policies. Based on this mapping, we demonstrate that distributing the best performing policies among characters improves the resulting simulations. Furthermore, we also propose a solution to dynamically adjust the policies, for each agent independently and while the simulation is running, based on the local context each agent is currently in. We demonstrate significant improvements of simulation results compared to previous work that would optimize parameters once for the whole simulation, or pick an optimized, but unique and static, policy for a given global simulation context.

Abstract Human motion synthesis and editing are essential to many applications like video games, virtual reality, and film post‐production. However, they often introduce artefacts in motion capture data, which can be detrimental to the perceived realism. In particular, footskating is a frequent and disturbing artefact, which requires knowledge of foot contacts to be cleaned up. Current approaches to obtain foot contact labels rely either on unreliable threshold‐based heuristics or on tedious manual annotation. In this article, we address automatic foot contact label detection from motion capture data with a deep learning based method. To this end, we first publicly release Under Pressure, a novel motion capture database labelled with pressure insoles data serving as reliable knowledge of foot contact with the ground. Then, we design and train a deep neural network to estimate ground reaction forces exerted on the feet from motion data and then derive accurate foot contact labels. The evaluation of our model shows that we significantly outperform heuristic approaches based on height and velocity thresholds and that our approach is much more robust when applied on motion sequences suffering from perturbations like noise or footskate. We further propose a fully automatic workflow for footskate cleanup: foot contact labels are first derived from estimated ground reaction forces. Then, footskate is removed by solving foot constraints through an optimisation‐based inverse kinematics (IK) approach that ensures consistency with the estimated ground reaction forces. Beyond footskate cleanup, both the database and the method we propose could help to improve many approaches based on foot contact labels or ground reaction forces, including inverse dynamics problems like motion reconstruction and learning of deep motion models in motion synthesis or character animation. Our implementation, pre‐trained model as well as links to database can be found at github.com/InterDigitalInc/UnderPressure.

Abstract Human character animation is often critical in entertainment content production, including video games, virtual reality or fiction films. To this end, deep neural networks drive most recent advances through deep learning (DL) and deep reinforcement learning (DRL). In this article, we propose a comprehensive survey on the state‐of‐the‐art approaches based on either DL or DRL in skeleton‐based human character animation. First, we introduce motion data representations, most common human motion datasets and how basic deep models can be enhanced to foster learning of spatial and temporal patterns in motion data. Second, we cover state‐of‐the‐art approaches divided into three large families of applications in human animation pipelines: motion synthesis, character control and motion editing. Finally, we discuss the limitations of the current state‐of‐the‐art methods based on DL and/or DRL in skeletal human character animation and possible directions of future research to alleviate current limitations and meet animators' needs.

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.

In virtual reality, several manipulation techniques distort users' motions, for example to reach remote objects or increase precision. These techniques can become problematic when used with avatars, as they create a mismatch between the real performed action and the corresponding displayed action, which can negatively impact the sense of embodiment. In this paper, we propose to use a dual representation during anisomorphic interaction. A co-located representation serves as a spatial reference and reproduces the exact users' motion, while an interactive representation is used for distorted interaction. We conducted two experiments, investigating the use of dual representations with amplified motion (with the Go-Go technique) and decreased motion (with the PRISM technique). Two visual appearances for the interactive representation and the co-located one were explored. This exploratory study investigating dual representations in this context showed that people globally preferred having a single representation, but opinions diverged for the Go-Go technique. Also, we could not find significant differences in terms of performance. While interacting seemed more important than showing exact movements for agency during out-of-reach manipulation, people felt more in control of the realistic arm during close manipulation.

Crowd motion data is fundamental for understanding and simulating realistic crowd behaviours. Such data is usually collected through controlled experiments to ensure that both desired individual interactions and collective behaviours can be observed. It is however scarce, due to ethical concerns and logistical difficulties involved in its gathering, and only covers a few typical crowd scenarios. In this work, we propose and evaluate a novel Virtual Reality based approach lifting the limitations of real-world experiments for the acquisition of crowd motion data. Our approach immerses a single user in virtual scenarios where he/she successively acts each crowd member. By recording the past trajectories and body movements of the user, and displaying them on virtual characters, the user progressively builds the overall crowd behaviour by him/herself. We validate the feasibility of our approach by replicating three real experiments, and compare both the resulting emergent phenomena and the individual interactions to existing real datasets. Our results suggest that realistic collective behaviours can naturally emerge from virtual crowd data generated using our approach, even though the variety in behaviours is lower than in real situations. These results provide valuable insights to the building of virtual crowd experiences, and reveal key directions for further improvements.

Avatars, the users’ virtual representations, are becoming ubiquitous in virtual reality applications. In this context, the avatar becomes the medium which enables users to manipulate objects in the virtual environment. It also becomes the users’ main spatial reference, which can not only alter their interaction with the virtual environment, but also the perception of themselves. In this paper, we review and analyse the current state-of-the-art for 3D object manipulation and the sense of embodiment. Our analysis is twofold. First, we discuss the impact that the avatar can have on object manipulation. Second, we discuss how the different components of a manipulation technique (i.e. input, control and feedback) can influence the user’s sense of embodiment. Throughout the analysis, we crystallise our discussion with practical guidelines for VR application designers and we propose several research topics towards “avatar-friendly’’ manipulation techniques.

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.

Virtual human crowds are regularly featured in movies and video games. With a large number of virtual characters each behaving in their own way, spectacular scenes can be produced. The more diverse the characters and their behaviors are, the more realistic the virtual crowd is expected to be perceived. Hence, creating virtual crowds is a trade-off between the cost associated with acquiring more diverse assets, namely more virtual characters with their animations, and achieving better realism. In this paper, our focus is on the perceived variety in virtual crowd character motions. We present an experiment exploring whether observers are able to identify virtual crowds including motion clones in the case of large-scale crowds (from 250 to 1000 characters). As it is not possible to acquire individual motions for such numbers of characters, we rely on a state-of-the-art motion variation approach to synthesize unique variations of existing examples for each character in the crowd. Participants then compared pairs of videos, where each character was animated either with a unique motion or using a subset of these motions. Our results show that virtual crowds with more than two motions (one per gender) were perceptually equivalent, regardless of their size. We believe these findings can help create efficient crowd applications, and are an additional step into a broader understanding of the perception of motion variety.

In this article, we introduce a concept called "virtual co-embodiment", which enables a user to share their virtual avatar with another entity (e.g., another user, robot, or autonomous agent). We describe a proof-of-concept in which two users can be immersed from a first-person perspective in a virtual environment and can have complementary levels of control (total, partial, or none) over a shared avatar. In addition, we conducted an experiment to investigate the influence of users' level of control over the shared avatar and prior knowledge of their actions on the users' sense of agency and motor actions. The results showed that participants are good at estimating their real level of control but significantly overestimate their sense of agency when they can anticipate the motion of the avatar. Moreover, participants performed similar body motions regardless of their real control over the avatar. The results also revealed that the internal dimension of the locus of control, which is a personality trait, is negatively correlated with the user's perceived level of control. The combined results unfold a new range of applications in the fields of virtual-reality-based training and collaborative teleoperation, where users would be able to share their virtual body.

Synthesizing motions that look realistic and diverse is a challenging task in animation. Therefore, a few generic walking motions are typically used when creating crowds of walking virtual characters, leading to a lack of variations as motions are not necessarily adapted to each and every virtual character’s characteristics. While some attempts have been made to create variations, it appears necessary to identify the relevant parameters that influence users’ perception of such variations to keep a good trade-off between computational costs and realism. In this paper, we therefore investigate the ability of viewers to identify an invariant parameter of human walking named the Walk Ratio (step length to step frequency ratio), which was shown to be specific to each individual and constant for different speeds, but which has never been used to drive animations of virtual characters. To this end, we captured 2 female and 2 male actors walking at different freely chosen speeds, as well as at different combinations of step frequency and step length. We then performed a perceptual study to identify the Walk Ratio that was perceived as the most natural for each actor when animating a virtual character, and compared it to the Walk Ratio freely chosen by the actor during the motion capture session. We found that Walk Ratios chosen by observers were in the range of Walk Ratios measured in the literature, and that participants perceived differences between the Walk Ratios of animated male and female characters, as evidenced in the biomechanical literature. Our results provide new considerations to drive the animation of walking virtual characters using the Walk Ratio as a parameter, and might provide animators with novel means to control the walking speed of characters through simple parameters while retaining the naturalness of the locomotion.

While digital humans are key aspects of the rapidly evolving areas of virtual reality, gaming, and online communications, many applications would benefit from using digital personalized (stylized) representations of users, as they were shown to highly increase immersion, presence and emotional response. In particular, depending on the target application, one may want to look like a dwarf or an elf in a heroic fantasy world, or like an alien on another planet, in accordance with the style of the narrative. While creating such virtual replicas requires stylization of the user’s features onto the virtual character, no formal study has however been conducted to assess the ability to recognize stylized characters. In this paper, we present a perceptual study investigating the effect of the degree of stylization on the ability to recognize an actor, and the subjective acceptability of stylizations. Results show that recognition rates decrease when the degree of stylization increases, while acceptability of the stylization increases. These results provide recommendations to achieve good compromises between stylization and recognition, and pave the way to new stylization methods providing a tradeoff between stylization and recognition of the actor.

Virtual embodiment and navigation are two topics widely studied in the virtual reality community. Despite the potential inter-relation between embodiment and locomotion, studies on virtual navigation rarely supply users with a virtual representation, while studies on virtual embodiment rarely allow users to virtually navigate. This paper therefore explores this potential inter-relation by focusing on the two following questions: Does the locomotion technique have an impact on the user's sense of embodiment? Does embodying an avatar have an impact on the user's preference and performance depending on the locomotion technique?To address these questions, we conducted a user study (N=60) exploring the relationship between the locomotion technique and the user's sense of embodiment over a virtual avatar seen from a first-person perspective. Three widely used locomotion techniques were evaluated: real walking, walking-in-place and virtual steering. All participants performed four different tasks involving a different awareness of their virtual avatar. Participants also performed the same tasks without being embodied in an avatar. The results show that participants had a comparable sense of embodiment with all techniques when embodied in an avatar, and that the presence or absence of the virtual avatar did not alter their performance while navigating, independently of the technique. Taken together, our results represent a first attempt to qualify the inter-relation between virtual navigation and virtual embodiment, and suggest that the 3D locomotion technique used has little influence on the user's sense of embodiment in VR.

When we are walking in crowds, we mainly use visual information to avoid collisions with other pedestrians. Thus, gaze activity should be considered to better understand interactions between people in a crowd. In this work, we use Virtual Reality (VR) to facilitate motion and gaze tracking, as well as to accurately control experimental conditions, in order to study the effect of crowd density on eye-gaze behavior. Our motivation is to better understand how interaction neighborhood (i.e., the subset of people actually influencing one’s locomotion trajectory) changes with density. To this end, we designed two experiments. The first one evaluates the biases introduced by the use of VR on the visual activity when walking among people, by comparing eye-gaze activity while walking in a real and virtual street. We then designed a second experiment where participants walked in a virtual street with different levels of pedestrian density. We demonstrate that gaze fixations are performed at the same frequency despite increases in pedestrian density, while the eyes scan a narrower portion of the street. These results suggest that in such situations walkers focus more on people in front and closer to them. These results provide valuable insights regarding eye-gaze activity during interactions between people in a crowd, and suggest new recommendations in designing more realistic crowd simulations.

In human interaction, people will keep different distances from each other depending on their gender. For example, males will stand further away from males and closer to females. Previous studies in virtual reality (VR), where people were interacting with virtual humans, showed a similar result. However, many other variables influence proximity, such as appearance characteristics of the virtual character (e.g., attractiveness). Our study focuses on proximity to virtual walkers, where gender could be recognised from motion only, since previous studies using point-light displays found walking motion is rich in gender cues. In our experiment, a walking wooden mannequin approached the participant embodied in a virtual avatar using the HTC Vive Pro HMD and controllers. The mannequin animation was motion captured from several male and female actors and each motion was displayed individually on the character. Participants used the controller to stop the approaching mannequin when they felt it was uncomfortably close to them. Based on previous work, we hypothesised that proximity will be affected by the gender of the character, but unlike previous research, the gender in our experiment could only be determined from character’s motion. We also expected differences in proximity according to the gender of the participant. We additionally expected some motions to be rated more attractive than others and that attractive motions would reduce the proximity measure. Our results show support for the last two assumptions, but no difference in proximity was found according to the gender of the character’s motion. Our findings have implications for the design of virtual characters in interactive virtual environments.

In Virtual Reality, a number of studies have been conducted to assess the influence of avatar appearance, avatar control and user point of view on the Sense of Embodiment (SoE) towards a virtual avatar. However, such studies tend to explore each factor in isolation. This paper aims to better understand the inter-relations among these three factors by conducting a subjective matching experiment. In the presented experiment ( n=40), participants had to match a given "optimal" SoE avatar configuration (realistic avatar, full-body motion capture, first-person point of view), starting by a "minimal" SoE configuration (minimal avatar, no control, third-person point of view), by iteratively increasing the level of each factor. The choices of the participants provide insights about their preferences and perception over the three factors considered. Moreover, the subjective matching procedure was conducted in the context of four different interaction tasks with the goal of covering a wide range of actions an avatar can do in a VE. The paper also describes a baseline experiment ( n=20) which was used to define the number and order of the different levels for each factor, prior to the subjective matching experiment (e.g. different degrees of realism ranging from abstract to personalised avatars for the visual appearance). The results of the subjective matching experiment show that point of view and control levels were consistently increased by users before appearance levels when it comes to enhancing the SoE. Second, several configurations were identified with equivalent SoE as the one felt in the optimal configuration, but vary between the tasks. Taken together, our results provide valuable insights about which factors to prioritize in order to enhance the SoE towards an avatar in different tasks, and about configurations which lead to fulfilling SoE in VE.

With the increasing use of avatars (i.e. the virtual representation of the user in a virtual environment) in virtual reality, it is important to identify the factors eliciting the sense of embodiment or the factors that can disrupt this feeling. This paper reports an exploratory study aiming at identifying internal factors (personality traits and body awareness) that might cause either a resistance or a predisposition to feel a sense of embodiment towards a virtual avatar. To this purpose, we conducted an experiment (n=123) in which participants were immersed in a virtual environment and embodied in a gender-matched generic virtual avatar through a head-mounted display. After an exposure phase in which they had to perform a number of visuomotor tasks (during 2 minutes) a virtual character entered the virtual scene and stabbed the participants' virtual hand with a knife. The participants' sense of embodiment was measured, as well as several personality traits (Big Five traits and locus of control) and body awareness, to evaluate the influence of participants' personality on the acceptance of the virtual body. The major finding of the experiment is that the locus of control is linked to several components of embodiment: the sense of agency is positively correlated with an internal locus of control and the sense of body ownership is positively correlated with an external locus of control. Interestingly, both components are not influenced by the same traits, which confirms that they can appear independently. Taken together our results suggest that the locus of control could be a good predictor of the sense of embodiment when the user embodies an avatar with a similar physical appearance. Yet, further studies are required to confirm these results.

No abstract available.

Simulating realistic interactions between virtual characters has been of interest to research communities for years, and is particularly important to automatically populate virtual environments. This problem requires to accurately understand and model how humans interact, which can be difficult to assess. In this context, Virtual Reality (VR) is a powerful tool to study human behaviour, especially as it allows assessing conditions which are both ecological and controlled. While VR was shown to allow realistic collision avoidance adaptations, in the frame of the ecological theory of perception and action, interactions between walkers can not solely be characterized through motion adaptations but also through the perception processes involved in such interactions. The objective of this paper is therefore to evaluate how different VR setups influence gaze behaviour during collision avoidance tasks between walkers. To this end, we designed an experiment involving a collision avoidance task between a participant and another walker (real confederate or virtual character). During this task, we compared both the partici-pant`s locomotion and gaze behaviour in a real environment and the same situation in different VR setups (including a CAVE, a screen and a Head-Mounted Display). Our results show that even if some quantitative differences exist, gaze behaviour is qualitatively similar between VR and real conditions. Especially, gaze behaviour in VR setups including a HMD is more in line with the real situation than the other setups. Furthermore, the outcome on motion adaptations confirms previous work, where collision avoidance behaviour is qualitatively similar in VR and real conditions. In conclusion, our results show that VR has potential for qualitative analysis of locomotion and gaze behaviour during collision avoidance. This opens perspectives in the design of new experiments to better understand human behaviour, in order to design more realistic virtual humans.

No abstract available.

The selection of relevant features in large databases is one of the most important and challenging problems in data mining.Samples forming a given database are generally described by a predefined set of features, and the situation where not all such features can be used for classification purposes needs very often to be faced in real applications.This situation is very typical when the database is related to a phenomenon whose characteristics are not well known.In this context, the extraction of relevant features can therefore also provide additional information on the studied phenomena.We tackle the feature selection problem from an optimization point of view, by reducing it to the problem of finding a maximal consistent "clustering" grouping together the samples and the features of the database.In this work, we extend this approach to dynamical databases, where features are not represented by only one real value, but they are rather given as sequences of a predefined number of real values.Our main contribution consists in proposing an alternative representation of the database so that it fits with a tridimensional matrix with no missing entries, from which a consistent triclustering can be obtained.

Retargeting motion from one character to another is a key process in computer animation. It enables to reuse animations designed for a character to animate another one, or to make performance-driven be faithful to what has been performed by the user. Previous work mainly focused on retargeting skeleton animations whereas the contextual meaning of the motion is mainly linked to the relationship between body surfaces, such as the contact of the palm with the belly. In this paper we propose a new context-aware motion retargeting framework, based on deforming a target character to mimic a source character poses using harmonic mapping. We also introduce the idea of Context Graph: modeling local interactions between surfaces of the source character, to be preserved in the target character, in order to ensure fidelity of the pose. In this approach, no rigging is required as we directly manipulate the surfaces, which makes the process totally automatic. Our results demonstrate the relevance of this automatic rigging-less approach on motions with complex contacts and interactions between the character's surface.

In this paper, we explore the influence of sharing a virtual environment with another user on the sense of embodiment in virtual reality. For this aim, we conducted an experiment where users were immersed in a virtual environment while being embodied in an anthropomorphic virtual representation of themselves. To evaluate the influence of the presence of another user, two situations were studied: either users were immersed alone, or in the company of another user. During the experiment, participants performed a virtual version of the well-known whac-a-mole game, therefore interacting with the virtual environment, while sitting at a virtual table. Our results show that users were significantly more “efficient” (i.e., faster reaction times), and accordingly more engaged, in performing the task when sharing the virtual environment, in particular for the more competitive tasks. Also, users experienced comparable levels of embodiment both when immersed alone or with another user. These results are supported by subjective questionnaires but also through behavioural responses, e.g. users reacting to the introduction of a threat towards their virtual body. Taken together, our results show that competition and shared experiences involving an avatar do not influence the sense of embodiment, but can increase user engagement. Such insights can be used by designers of virtual environments and virtual reality applications to develop more engaging applications.

Human-like characters can be modeled by suitable skeletal structures, which basically consist in trees where edges represent bones and vertices are joints between two adjacent bones.Motion is then defined as variations of the joints' configuration (i.e., partial rotations) over time, which also influences joint positions.However, this representation does not allow to easily represent the relationship between joints that are not directly connected by a bone.This work is therefore based on the premise that variations of the relative distances between such joints are important to represent complex human motions.While the former representations are currently used in practice for playing and analyzing motions, the latter can help in modeling a new class of problems where the relationships in human motions need to be simulated.Our main interest in this work is in adapting previously captured human postures (one frame of a given motion) with the aim of satisfying a certain number of geometrical constraints, which turn out to be easily definable in terms of distances.We present a novel procedure for approximating the relative inter-joint distances for skeletal structures having arbitrary features and respecting a predefined posture.This set of inter-joint distances defines an instance of the Distance Geometry Problem (DGP), that we tackle with a non-monotone spectral gradient method.

In character animation, it is often the case that motions created or captured on a specific morphology need to be reused on characters having a different morphology while maintaining specific relationships such as body contacts or spatial relationships between body parts. This process, called motion retargeting, requires determining which body part relationships are important in a given animation. This paper presents a novel frame-based approach to motion retargeting which relies on a normalized representation of body joints distances. We propose to abstract postures by computing all the inter-joint distances of each animation frame and store them in Euclidean Distance Matrices (EDMs). They 1) present the benefits of capturing all the subtle relationships between body parts, 2) can be adapted through a normalization process to create a morphology-independent distance-based representation, and 3) can be used to efficiently compute retargeted joint positions best satisfying newly computed distances. We demonstrate that normalized EDMs can be efficiently applied to a different skeletal morphology by using a Distance Geometry Problem (DGP) approach, and present results on a selection of motions and skeletal morphologies. Our approach opens the door to a new formulation of motion retargeting problems, solely based on a normalized distance representation.

How do people appropriate their virtual hand representation when interacting in virtual environments? In order to answer this question, we conducted an experiment studying the sense of embodiment when interacting with three different virtual hand representations, each one providing a different degree of visual realism but keeping the same control mechanism. The main experimental task was a Pick-and-Place task in which participants had to grasp a virtual cube and place it to an indicated position while avoiding an obstacle (brick, barbed wire or fire). An additional task was considered in which participants had to perform a potentially dangerous operation towards their virtual hand: place their virtual hand close to a virtual spinning saw. Both qualitative measures and questionnaire data were gathered in order to assess the sense of agency and ownership towards each virtual hand. Results show that the sense of agency is stronger for less realistic virtual hands which also provide less mismatch between the participant's actions and the animation of the virtual hand. In contrast, the sense of ownership is increased for the human virtual hand which provides a direct mapping between the degrees of freedom of the real and virtual hand.

A typical crowd engine pipeline animates numerous moving characters according to a two-step process: global trajectories are generated by a crowd simulator, whereas full body motions are generated by animation engines. Because interactions are only considered at the first stage, animations sometimes lead to residual collisions and/or characters walking as if they were alone, showing no sign to the influence of others. In this paper, we investigate the value of adding shoulder motions to characters passing at close distances on the perceived visual quality of crowd animations (i.e., perceived residual collisions and animation naturalness). We present two successive perceptual experiments exploring this question where we investigate first, local interactions between two isolated characters, and second, crowd scenarios. The first experiment shows that shoulder motions have a strong positive effect on both perceived residual collisions and animation naturalness. The second experiment demonstrates that the effect of shoulder motions on animation naturalness is preserved in the context of crowd scenarios, even though the complexity of the scene is largely increased. Our general conclusion is that adding secondary motions in character interactions has a significant impact on the visual quality of crowd animations, with a very light impact on the computational cost of the whole animation pipeline. Our results advance crowd animation techniques by enhancing the simulation of complex interactions between crowd characters with simple secondary motion triggering techniques.

A better understanding of how intentions and traits are perceived from body movements is required for the design of more effective virtual characters that behave in a socially realistic manner. For this purpose, realistic body motion, captured from human movements, is being used more frequently for creating characters with natural animations in games and entertainment. However, it is not always clear for programmers and designers which specific motion parameters best convey specific information such as certain emotions, intentions, or traits. We conducted two experiments to investigate whether the perceived traits of actors could be determined from their body motion, and whether these traits were associated with their perceived intentions. We first recorded body motions from 26 professional actors, who were instructed to move in a “hero”-like or a “villain”-like manner. In the first experiment, 190 participants viewed individual video recordings of these actors and were required to provide ratings to the body motion stimuli along a series of different cognitive dimensions (intentions, attractiveness, dominance, trustworthiness, and distinctiveness). The intersubject ratings across observers were highly consistent, suggesting that social traits are readily determined from body motion. Moreover, correlational analyses between these ratings revealed consistent associations across traits, for example, that perceived “good” intentions were associated with higher ratings of attractiveness and dominance. Experiment 2 was designed to elucidate the qualitative body motion cues that were critical for determining specific intentions and traits from the hero- and villain-like body movements. The results revealed distinct body motions that were readily associated with the perception of either “good” or “bad” intentions. Moreover, regression analyses revealed that these ratings accurately predicted the perception of the portrayed character type. These findings indicate that intentions and social traits are communicated effectively via specific sets of body motion features. Furthermore, these results have important implications for the design of the motion of virtual characters to convey desired social information.

In this article, we investigate the perception of gender from the motion of virtual humans under different emotional conditions and explore the effect of emotional bias on gender perception (e.g., anger being attributed to males more than females). As motion types can present different levels of physiological cues, we also explore how two types of motion (walking and conversations) are affected by emotional bias. Walking typically displays more physiological cues about gender (e.g., hip sway) and therefore is expected to be less affected by emotional bias. To investigate these effects, we used a corpus of captured facial and body motions from four male and four female actors, performing basic emotions through conversation and walk. We expected that the appearance of the model would also influence gender perception; therefore, we displayed both male and female motions on two virtual models of different sex. Two experiments were then conducted to assess gender judgments from these motions. In both experiments, participants were asked to rate how male or female they considered the motions to be under different emotional states, then classified the emotions to determine how accurately they were portrayed by actors. Overall, both experiments showed that gender ratings were affected by the displayed emotion. However, we found that conversations were influenced by gender stereotypes to a greater extent than walking motions. This was particularly true for anger, which was perceived as male on both male and female motions, and sadness, which was perceived as less male when portrayed by male actors. We also found a slight effect of the model when observing gender on different types of virtual models. These results have implications for the design and animation of virtual humans.

ABSTRACT Humans adjust their movements in advance to prepare for the forthcoming action, resulting in efficient and smooth transitions. However, traditional computer animation approaches such as motion graphs simply concatenate a series of actions without taking into account the following one. In this paper, we propose a new method to produce preparation behaviors using reinforcement learning. As an offline process, the system learns the optimal way to approach a target and to prepare for interaction. A scalar value called the level of preparation is introduced, which represents the degree of transition from the initial action to the interacting action. To synthesize the movements of preparation, we propose a customized motion blending scheme based on the level of preparation, which is followed by an optimization framework that adjusts the posture to keep the balance. During runtime, the trained controller drives the character to move to a target with the appropriate level of preparation, resulting in a humanlike behavior. We create scenes in which the character has to move in a complex environment and to interact with objects, such as crawling under and jumping over obstacles while walking. The method is useful not only for computer animation but also for real‐time applications such as computer games, in which the characters need to accomplish a series of tasks in a given environment. Copyright © 2013 John Wiley & Sons, Ltd.

Animation budget constraints during the development of a game often call for the use of a limited set of generic motions. Editing operations are thus generally required to animate virtual characters with a sufficient level of variety. Evaluating the perceptual plausibility of edited animations can therefore contribute greatly towards producing visually plausible animations. In this article, we study observers’ sensitivity to manipulations of overarm and underarm biological throwing animations. In the first experiment, we modified the release velocity of the ball while leaving the motion of the virtual thrower and the angle of release of the ball unchanged. In the second experiment, we evaluated the possibility of further modifying throwing animations by simultaneously editing the motion of the thrower and the release velocity of the ball, using dynamic time warping. In both experiments, we found that participants perceived shortened underarm throws to be particularly unnatural. We also found that modifying the thrower's motion in addition to modifying the release velocity of the ball does not significantly improve the perceptual plausibility of edited throwing animations. In the third experiment, we modified the angle of release of the ball while leaving the magnitude of release velocity and the motion of the thrower unchanged, and found that this editing operation is efficient for improving the perceptual plausibility of shortened underarm throws. Finally, in Experiment 4, we replaced the virtual human thrower with a mechanical throwing device (a ramp) and found the opposite pattern of sensitivity to modifications of the release velocity, indicating that biological and physical throws are subject to different perceptual rules. Our results provide valuable guidelines for developers of games and virtual reality applications by specifying thresholds for the perceptual plausibility of throwing manipulations while also providing several interesting insights for researchers in visual perception of biological motion.

Better understanding of how social traits and intentions are conveyed through human body motion is central in creating believable, life-like virtual characters. Body motion conveys socially relevant information on which people make judgments, such as other people's personality or gender. [e.g. Koppensteiner and Grammer 2011, Thoresen et al. 2012]. However, it is unclear how the body motion features are related to the perceived social traits and intentions of others. The main goal of this study is to examine how visual information from human body motion is related to social traits and intention perception. Furthermore, we also investigated if body motion features can be used as predictors of social intentions.

Variations in the timing and speed of movements in human interactions carry important social information. For example, seeing one player push another player at a football game, we can deduce whether the player being pushed resisted or anticipated the oncoming push based on subtle differences in the timing and velocity of movements of both players. With the development of computer animations of human characters, it is important to understand the sensitivity and limits of human perception in such interactions to accurately portray human interactions.

In this paper, we investigate the ability of humans to determine the gender of conversing characters, based on facial and body cues for emotion. We used a corpus of simultaneously captured facial and body motions from four male and four female actors. In our Gender Rating task, participants were asked to rate how male or female they considered the motions to be, under different emotional states. In our Emotion Recognition task, participants were asked to classify the emotions, in order to determine how accurately perceived those emotions were. We found that gender perception was affected by emotion, where certain emotions facilitated gender determination while others masked it. We also found that there was no correlation between how accurate an emotion was portrayed and how much gender information was present in that motion. Finally, we found that the model used to display the motion did not affect gender perception of motion but did alter emotion recognition.

It has been shown that humans are sensitive to the portrayal of emotions for virtual characters. However, previous work in this area has often examined this sensitivity using extreme examples of facial or body animation. Less is known about how attuned people are at recognizing emotions as they are expressed during conversational communication. In order to determine whether body or facial motion is a better indicator for emotional expression for game characters, we conduct a perceptual experiment using synchronized full-body and facial motion-capture data. We find that people can recognize emotions from either modality alone, but combining facial and body motion is preferable in order to create more expressive characters.

Recent advances in rendering and data-driven animation have enabled the creation of compelling characters with impressive levels of realism. While data-driven techniques can produce animations that are extremely faithful to the original motion, many challenging problems remain because of the high complexity of human motion. A better understanding of the factors that make human motion recognizable and appealing would be of great value in industries where creating a variety of appealing virtual characters with realistic motion is required. To investigate these issues, we captured thirty actors walking, jogging and dancing, and applied their motions to the same virtual character (one each for the males and females). We then conducted a series of perceptual experiments to explore the distinctiveness and attractiveness of these human motions, and whether characteristic motion features transfer across an individual's different gaits. Average faces are perceived to be less distinctive but more attractive, so we explored whether this was also true for body motion. We found that dancing motions were most easily recognized and that distinctiveness in one gait does not predict how recognizable the same actor is when performing a different motion. As hypothesized, average motions were always amongst the least distinctive and most attractive. Furthermore, as 50% of participants in the experiment were Caucasian European and 50% were Asian Korean, we found that the latter were as good as or better at recognizing the motions of the Caucasian actors than their European counterparts, in particular for dancing males, whom they also rated more highly for attractiveness.

Understanding whether observers are sensitive to physical distortions in dynamic events is important in order to develop plausible simulations while saving time on details that observers cannot perceive [Barzel et al. 1996]. In this experiment, we evaluated observers' sensitivity to physical distortions in virtual throwing animations. As previous results showed a greater sensitivity to physical distortions on virtual human characters rather than on simple objects [Reitsma et al. 2008], we studied the effect of using both a biological human throw and a simple physical simulation (free fall of a ball on a ramp). The results show that observers' sensitivity to errors depends on the animacy of the thrower.

Subtle animation details such as finger or facial movements help to bring virtual characters to life and increase their appeal. However, it is not always possible to capture finger animations simultaneously with full-body motion, due to limitations of the setup or tight production schedules. Therefore, hand motions are often either omitted, manually created by animators, or captured during a separate session and spliced with full body animation. In this paper, we investigate the perceived fidelity of hand animations where all the degrees of freedom of the hands are computed from reduced marker sets. In a set of perceptual experiments, we found that finger motions reconstructed with inverse kinematics from a reduced marker set of eight markers per hand are perceived to be very similar to the corresponding motions reconstructed using a full set of twenty markers. We demonstrate how using this reduced set of eight large markers enabled us to capture the finger and full-body motions of two actors performing a range of relatively unconstrained actions using a 13-camera motion capture system. This serves to simplify the capture process and to significantly reduce the time for cleanup, while preserving the natural biological movements of the hands relative to the actions performed.

With recent advances in real-time graphics technology, more realistic, believable and appealing virtual characters are needed than ever before. Both player-controlled avatars and non-player characters are now starting to interact with the environment, other virtual humans and crowds. However, simulating physical contacts between characters and matching appropriate reactions to specific actions is a highly complex problem, and timing errors, force mismatches and angular distortions are common. To investigate the effect of such anomalies on the perceived realism of two-character interactions, we captured a motion corpus of pushing animations and corresponding reactions and then conducted a series of perceptual experiments. We found that participants could easily distinguish between five different interaction forces, even when only one of the characters was visible. Furthermore, they were sensitive to all three types of anomalous interactions: timing errors of over 150ms were acceptable less than 50% of the time, with early or late reactions being equally perceptible; participants could perceive force mismatches, though over-reactions were more acceptable than under-reactions; finally, angular distortions when a character reacts to a pushing force reduce the acceptability of the interactions, but there is some evidence for a preference of expansion away from the pushing character's body. Our results provide insights to aid in designing motion capture sessions, motion editing strategies and balancing animation budgets.

Perception of biological motions is a key issue in order to evaluate the quality and the credibility of motions of virtual humans. This paper presents a perceptual study to evaluate if human beings are able to accurately distinguish differences in natural lifting motions with various masses in virtual environments (VE), which is not the case. However, they reached very close levels of accuracy when watching to computer animations compared to videos. Still, quotes of participants suggest that the discrimination process is easier in videos of real motions which included muscles contractions, more degrees of freedom, etc. These results can be used to help animators to design efficient physically-based animations.

Abstract This paper presents a novel, parameteric framework for synthesizing new character motions from existing motion capture data. Our framework can conduct morphological adaptation as well as kinematic and physically‐based corrections. All these solvers are organized in layers in order to be easily combined together. Given locomotion as an example, the system automatically adapts the motion data to the size of the synthetic figure and to its environment; the character will correctly step over complex ground shapes and counteract with external forces applied to the body. Our framework is based on a frame‐based solver. This ensures animating hundreds of humanoids with different morphologies in real‐time. It is particularly suitable for interactive applications such as video games and virtual reality where a user interacts in an unpredictable way. Copyright © 2009 John Wiley & Sons, Ltd.

This paper aims at proposing a new method to animate aerial motions in interactive environments while taking dynamics into account. Classical approaches are based on spacetime constraints and require a complete knowledge of the motion. However, in Virtual Reality, the user's actions are unpredictable so that such techniques cannot be used. In this paper, we deal with the simulation of gymnastic aerial motions in virtual reality. A user can directly interact with the virtual gymnast thanks to a real-time motion capture system. The user's arm motions are blended to the original aerial motions in order to verify their consequences on the virtual gymnast's performance. Hence, a user can select an initial motion, an initial velocity vector, an initial angular momentum, and a virtual character. Each of these choices has a direct influence on mechanical values such as the linear and angular momentum. We thus have developed an original method to adapt the character's poses at each time step in order to make these values compatible with mechanical laws: the angular momentum is constant during the aerial phase and the linear one is determined at take-off. Our method enables to animate up to 16 characters at 30hz on a common PC. To sum-up, our method enables to solve kinematic constraints, to retarget motion and to correct it to satisfy mechanical laws. The virtual gymnast application described in this paper is very promising to help sports-men getting some ideas which postures are better during the aerial phase for better performance.