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

International audienceIn 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

Lip syncing method for realistic expressive three-dimensional face model

Lip synchronization of 3D face model is now being used in a multitude of important fields. It brings a more human and dramatic reality to computer games, films and interactive multimedia, and is growing in use and importance. High level realism can be used in demanding applications such as computer games and cinema. Authoring lip syncing with complex and subtle expressions is still difficult and fraught with problems in terms of realism. Thus, this study proposes a lip syncing method of realistic expressive 3D face model. Animated lips require a 3D face model capable of representing the movement of face muscles during speech and a method to produce the correct lip shape at the correct time. The 3D face model is designed based on MPEG-4 facial animation standard to support lip syncing that is aligned with input audio file. It deforms using Raised Cosine Deformation function that is grafted onto the input facial geometry. This study also proposes a method to animate the 3D face model over time to create animated lip syncing using a canonical set of visemes for all pairwise combinations of a reduced phoneme set called ProPhone. Finally, this study integrates emotions by considering both Ekman model and Plutchik’s wheel with emotive eye movements by implementing Emotional Eye Movements Markup Language to produce realistic 3D face model. The experimental results show that the proposed model can generate visually satisfactory animations with Mean Square Error of 0.0020 for neutral, 0.0024 for happy expression, 0.0020 for angry expression, 0.0030 for fear expression, 0.0026 for surprise expression, 0.0010 for disgust expression, and 0.0030 for sad expression

Data-driven Communicative Behaviour Generation: A Survey

The development of data-driven behaviour generating systems has recently become the focus of considerable attention in the fields of human–agent interaction and human–robot interaction. Although rule-based approaches were dominant for years, these proved inflexible and expensive to develop. The difficulty of developing production rules, as well as the need for manual configuration to generate artificial behaviours, places a limit on how complex and diverse rule-based behaviours can be. In contrast, actual human–human interaction data collected using tracking and recording devices makes humanlike multimodal co-speech behaviour generation possible using machine learning and specifically, in recent years, deep learning. This survey provides an overview of the state of the art of deep learning-based co-speech behaviour generation models and offers an outlook for future research in this area.</jats:p

Modeling Multimodal Behaviors from Speech Prosody

International audienceHead and eyebrow movements are an important communication mean. They are highly synchronized with speech prosody. Endowing virtual agent with synchronized verbal and nonverbal behavior enhances their communicative performance. In this paper, we propose an animation model for the virtual agent based on a statistical model linking speech prosody and facial movement. A fully parameterized Hidden Markov Model is proposed first to capture the tight relationship between speech and facial movement of a human face extracted from a video corpus and then to drive automatically virtual agent's behaviors from speech signals. The correlation between head and eyebrow movements is also taken into account during the building of the model. Subjective and objective evaluations were conducted to validate this model