Expressive Body Animation Pipeline for Virtual Agent

Huang, Jing; Pélachaud, Catherine

doi:10.1007/978-3-642-33197-8_36

Cited by 15 publications

(7 citation statements)

References 18 publications

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“…Each behavior corresponds to a particular combination of AUs and BAPs. To avoid uncanny animations (e.g., when the agent’s expression freezes) and to obtain smooth transitions between two behaviors, we designed an interpolation function (based on Huang and Pelachaud, 2012 ) so that the previous behavior fades away while the new one slowly appears, resulting in a blended behavior.…”

Section: Virtual Agentmentioning

confidence: 99%

Affective Interaction with a Virtual Character Through an fNIRS Brain-Computer Interface

Aranyi

Pecune

Charles

et al. 2016

Front. Comput. Neurosci.

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Affective brain-computer interfaces (BCI) harness Neuroscience knowledge to develop affective interaction from first principles. In this article, we explore affective engagement with a virtual agent through Neurofeedback (NF). We report an experiment where subjects engage with a virtual agent by expressing positive attitudes towards her under a NF paradigm. We use for affective input the asymmetric activity in the dorsolateral prefrontal cortex (DL-PFC), which has been previously found to be related to the high-level affective-motivational dimension of approach/avoidance. The magnitude of left-asymmetric DL-PFC activity, measured using functional near infrared spectroscopy (fNIRS) and treated as a proxy for approach, is mapped onto a control mechanism for the virtual agent’s facial expressions, in which action units (AUs) are activated through a neural network. We carried out an experiment with 18 subjects, which demonstrated that subjects are able to successfully engage with the virtual agent by controlling their mental disposition through NF, and that they perceived the agent’s responses as realistic and consistent with their projected mental disposition. This interaction paradigm is particularly relevant in the case of affective BCI as it facilitates the volitional activation of specific areas normally not under conscious control. Overall, our contribution reconciles a model of affect derived from brain metabolic data with an ecologically valid, yet computationally controllable, virtual affective communication environment.

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Section: Virtual Agentmentioning

confidence: 99%

Affective Interaction with a Virtual Character Through an fNIRS Brain-Computer Interface

Aranyi

Pecune

Charles

et al. 2016

Front. Comput. Neurosci.

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“…la manière avec laquelle le mouvement a été effectué Dans les travaux existants, les paramètres qui contrôlent l'expressivité du mouvement se limitent généralement à un ensemble de paramètres d'expressivité calculés à partir de données de capture de mouvement pour un type de geste particulier [Roether et al, 2009]. Cependant, ces paramètres sont souvent associés à un nombre limité de types de geste et ne peuvent pas être étendus à l'ensemble des gestes possibles d'un ACA D'autres travaux proposent de contrôler l'expressivité des mouvements de l'ACA (par exemple leur amplitude) à travers des fonctions mathématiques reposant sur quelques lois invariantes du mouvement humain [Huang and Pelachaud 2012] Nous proposons un modèle de comportement émotionnel reposant sur l'analyse d'un corpus de capture de mouvements. Inspiré du système de codage défini dans [Dael et al, 2012], la définition des paramètres que nous avons adoptée pour contrôler le comportement émotionnel corporel d'un ACA repose sur plusieurs niveaux de description, à la fois pour décrire un mouvement corporel et pour décrire les caractéristiques de ce mouvement.…”

Section: Expressions Corporelles Affectivesunclassified

Vers des Agents Conversationnels Animés dotés d'émotions et d'attitudes sociales

Ochs

Ding

Fourati

et al. 2015

Journal d'Interaction Personne-Système

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International audience In this article, we propose an architecture of a socio-affective Embodied Conversational Agent (ECA). The different computational models of the architecture enable an ECA to express emotions and social attitudes during an interaction with a user. Based on corpora of actors expressing emotions, models have been defined to compute the emotional facial expressions of an ECA and the characteristics of its corporal movements. A user-perceptive approach has been used to design models to define how an ECA should adapt its non-verbal behavior according to the social attitude the ECA wants to display and the behavior of its interlocutor. The emotions and the social attitudes to express are computed by cognitive models presented in this article. Dans cet article, nous proposons une architecture d'un Agent Conversationnel Animé (ACA) socio-affectif. Les différents modèles computationnels sous-jacents à cette architecture, permettant de donner la capacité à un ACA d'exprimer des émotions et des attitudes sociales durant son interaction avec l'utilisateur, sont présentés. A partir de corpus d'individus exprimant des émotions, des modèles permettant de calculer l'expression faciale émotionnelle d'un ACA ainsi que les caractéristiques de ses mouvements du corps ont été définis. Fondés sur une approche centrée sur la perception de l'utilisateur, des modèles permettant de calculer comment un ACA doit adapter son comportement non-verbal suivant l'attitude sociale qu'il souhaite exprimer et suivant le comportement de son interlocuteur ont été construits. Le calcul des émotions et des attitudes sociales à exprimer est réalisé par des modèles cognitifs présentés dans cet article.

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“…In addition, usage of ECAs has been demonstrated to be potential in many fields besides the original interface agents in human computer interaction such as Rea (Cassell et al, 1999), Greta (Niewiadomski et al, 2009;Huang and Pelachaud, 2012), and RealActor (Čerekovič and Pandžič, 2011). For instance, a virtual human presenter is designed for weather forecasting, and slide presentation in the work of (Noma et al, 2000).…”

Section: Related Workmentioning

confidence: 99%

Accurate Synchronization of Gesture and Speech for Conversational Agents using Motion Graphs

Nagai

Takayama

et al. 2014

Proceedings of the 6th International Conference on Agents and Artificial Intelligence

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Multimodal representation of conversational agents requires accurate synchronization of gesture and speech. For this purpose, we investigate the important issues in synchronization as a practical guideline for our algorithm design through a precedent case study and propose a two-step synchronization approach. Our case study reveals that two issues (i.e. duration and timing) play an important role in the manual synchronizing of gesture with speech. Considering the synchronization problem as a motion synthesis problem instead of a behavior scheduling problem used in the conventional methods, we use a motion graph technique with constraints on gesture structure for coarse synchronization in a first step and refine this further by shifting and scaling the motion in a second step. This approach can successfully synchronize gesture and speech with respect to both duration and timing. We have confirmed that our system makes the creation of attractive content easier than manual creation of equal quality. In addition, subjective evaluation has demonstrated that the proposed approach achieves more accurate synchronization and higher motion quality than the state-of-the-art method.

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Expressive Body Animation Pipeline for Virtual Agent

Cited by 15 publications

References 18 publications

Affective Interaction with a Virtual Character Through an fNIRS Brain-Computer Interface

Affective Interaction with a Virtual Character Through an fNIRS Brain-Computer Interface

Vers des Agents Conversationnels Animés dotés d'émotions et d'attitudes sociales

Accurate Synchronization of Gesture and Speech for Conversational Agents using Motion Graphs

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