Browsing by Author "Hoyet, Ludovic"

Now showing 1 - 7 of 7
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    Dynamic Combination of Crowd Steering Policies Based on Context
    (The Eurographics Association and John Wiley & Sons Ltd., 2022) Cabrero-Daniel, Beatriz; Marques, Ricardo; Hoyet, Ludovic; Pettré, Julien; Blat, Josep; Chaine, Raphaëlle; Kim, Min H.
    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.
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    Exploring the Perception of Center of Mass changes for VR Avatars
    (The Eurographics Association, 2023) Vyas, Bharat; Hoyet, Ludovic; O'Sullivan, Carol; Jean-Marie Normand; Maki Sugimoto; Veronica Sundstedt
    Populating Virtual Environments with animated virtual characters often involves retargeting motions to 3D body models with differing shapes. A user's avatar, for example, should move in a way that is consistent with their model's body shape in order to maintain the sense of presence. We present a set of perception experiments to explore how motions captured from actors with various body mass indices (BMI) are perceived, when they are retargeted to characters with different BMIs. We also explored the perceptual effects of retargeting average and physics-based motions. To explore the latter, we devised a physics-based controller framework that utilizes motion, target body weight, and height as inputs to generate retargeted motions. Despite the controller generating varied motions for various body shapes, average motions consistently outperformed the controllergenerated motions in terms of naturalness. Overall, this work highlights an anthropometric based physics controller and a novel approach for perceptual evaluation of human motion retargeting for virtual characters.
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    Influence of Threat Occurrence and Repeatability on the Sense of Embodiment and Threat Response in VR
    (The Eurographics Association, 2020) Fribourg, Rebecca; Blanpied, Evan; Hoyet, Ludovic; Lécuyer, Anatole; Argelaguet, Ferran; Argelaguet, Ferran and McMahan, Ryan and Sugimoto, Maki
    Does virtual threat harm the Virtual Reality (VR) experience? In this paper, we explored the potential impact of threat occurrence and repeatability on users' Sense of Embodiment (SoE) and threat response. The main findings of our experiment are that the introduction of a threat does not alter users' SoE but might change their behaviour while performing a task after the threat occurrence. In addition, threat repetitions did not show any effect on users' subjective SoE, or subjective and objective responses to threat. Taken together, our results suggest that embodiment studies should expect potential change in participants behaviour while doing a task after a threat was introduced, but that threat introduction and repetition do not seem to impact the subjective measure of the SoE (user responses to questionnaires) nor the objective measure of the SoE (behavioural response to threat towards the virtual body).
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    Interaction Fields: Intuitive Sketch-based Steering Behaviors for Crowd Simulation
    (The Eurographics Association and John Wiley & Sons Ltd., 2022) Colas, Adèle; van Toll, Wouter; Zibrek, Katja; Hoyet, Ludovic; Olivier, Anne-Hélène; Pettré, Julien; Chaine, Raphaëlle; Kim, Min H.
    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 an interaction 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.
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    A Perceptually-Validated Metric for Crowd Trajectory Quality Evaluation
    (ACM, 2021) Daniel, Beatriz Cabrero; Marques, Ricardo; Hoyet, Ludovic; Pettré, Julien; Blat, Josep; Narain, Rahul and Neff, Michael and Zordan, Victor
    Simulating crowds requires controlling a very large number of trajectories and is usually performed using crowd motion algorithms for which appropriate parameter values need to be found. The study of the relation between parametric values for simulation techniques and the quality of the resulting trajectories has been studied either through perceptual experiments or by comparison with real crowd trajectories. In this paper, we integrate both strategies. A quality metric, QF, is proposed to abstract from reference data while capturing the most salient features that affect the perception of trajectory realism. QF weights and combines cost functions that are based on several individual, local and global properties of trajectories. These trajectory features are selected from the literature and from interviews with experts. To validate the capacity of QF to capture perceived trajectory quality, we conduct an online experiment that demonstrates the high agreement between the automatic quality score and non-expert users. To further demonstrate the usefulness of QF , we use it in a data-free parameter tuning application able to tune any parametric microscopic crowd simulation model that outputs independent trajectories for characters. The learnt parameters for the tuned crowd motion model maintain the influence of the reference data which was used to weight the terms of QF.
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    A Survey on Deep Learning for Skeleton‐Based Human Animation
    (© 2022 Eurographics ‐ The European Association for Computer Graphics and John Wiley & Sons Ltd, 2022) Mourot, Lucas; Hoyet, Ludovic; Le Clerc, François; Schnitzler, François; Hellier, Pierre; Hauser, Helwig and Alliez, Pierre
    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.
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    UnderPressure: Deep Learning for Foot Contact Detection, Ground Reaction Force Estimation and Footskate Cleanup
    (The Eurographics Association and John Wiley & Sons Ltd., 2022) Mourot, Lucas; Hoyet, Ludovic; Clerc, François Le; Hellier, Pierre; Dominik L. Michels; Soeren Pirk
    Human motion synthesis and editing are essential to many applications like video games, virtual reality, and film postproduction. 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 UNDERPRESSURE, 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.