Classification et Caractérisation de l'Expression Corporelle des Emotions dans des Actions Quotidiennes

The work conducted in this thesis can be summarized into four main steps.Firstly, we proposed a multi-level body movement notation system that allows the description ofexpressive body movement across various body actions. Secondly, we collected a new databaseof emotional body expression in daily actions. This database constitutes a large repository of bodilyexpression of emotions including the expression of 8 emotions in 7 actions, combining video andmotion capture recordings and resulting in more than 8000 sequences of expressive behaviors.Thirdly, we explored the classification of emotions based on our multi-level body movement notationsystem. Random Forest approach is used for this purpose. The advantage of using RandomForest approach in our work is double-fold : 1) reliability of the classification model and 2) possibilityto select a subset of relevant features based on their relevance measures. We also comparedthe automatic classification of emotions with human perception of emotions expressed in differentactions. Finally, we extracted the most relevant features that capture the expressive content of themotion based on the relevance measure of features returned by the Random Forest model. Weused this subset of features to explore the characterization of emotional body expression acrossdifferent actions. A Decision Tree model was used for this purpose.Ce travail de thèse peut être résumé en quatre étapes principales. Premièrement, nousavons proposé un système d’annotation multi-niveaux pour décrire le mouvement corporel expressif dansdifférentes actions. Deuxièmement, nous avons enregistré une base de données de l’expression corporelledes émotions dans des actions quotidiennes. Cette base de données constitue un large corpus de comportementsexpressifs considérant l’expression de 8 émotions dans 7 actions quotidiennes, combinant à la fois lesdonnées audio-visuelle et les données de capture de mouvement et donnant lieu à plus que 8000 séquencesde mouvement expressifs. Troisièmement, nous avons exploré la classification des émotions en se basantsur notre système d’annotation multi-niveaux. L’approche des forêts aléatoires est utilisée pour cette fin. L’utilisationdes forêts aléatoires dans notre travail a un double objectif : 1) la fiabilité du modèle de classification,et 2) la possibilité de sélectionner un sous-ensemble de paramètres pertinents en se basant sur la mesured’importance retournée par le modèle. Nous avons aussi comparé la classification automatique des émotionsavec la perception humaine des émotions exprimées dans différentes actions. Finalement, nous avonsextrait les paramètres les plus pertinents qui retiennent l’expressivité du mouvement en se basant sur la mesured’importance retournée par le modèle des forêts aléatoires. Nous avons utilisé ce sous-ensemble deparamètres pour explorer la caractérisation de l’expression corporelle des émotions dans différentes actionsquotidiennes. Un modèle d’arbre de décision a été utilisé pour cette fin

Towards affective computing that works for everyone

Missing diversity, equity, and inclusion elements in affective computing datasets directly affect the accuracy and fairness of emotion recognition algorithms across different groups. A literature review reveals how affective computing systems may work differently for different groups due to, for instance, mental health conditions impacting facial expressions and speech or age-related changes in facial appearance and health. Our work analyzes existing affective computing datasets and highlights a disconcerting lack of diversity in current affective computing datasets regarding race, sex/gender, age, and (mental) health representation. By emphasizing the need for more inclusive sampling strategies and standardized documentation of demographic factors in datasets, this paper provides recommendations and calls for greater attention to inclusivity and consideration of societal consequences in affective computing research to promote ethical and accurate outcomes in this emerging field.Comment: 8 pages, 2023 11th International Conference on Affective Computing and Intelligent Interaction (ACII

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Survey on Emotional Body Gesture Recognition

Automatic emotion recognition has become a trending research topic in the past decade. While works based on facial expressions or speech abound, recognizing affect from body gestures remains a less explored topic. We present a new comprehensive survey hoping to boost research in the field. We first introduce emotional body gestures as a component of what is commonly known as "body language" and comment general aspects as gender differences and culture dependence. We then define a complete framework for automatic emotional body gesture recognition. We introduce person detection and comment static and dynamic body pose estimation methods both in RGB and 3D. We then comment the recent literature related to representation learning and emotion recognition from images of emotionally expressive gestures. We also discuss multi-modal approaches that combine speech or face with body gestures for improved emotion recognition. While pre-processing methodologies (e.g., human detection and pose estimation) are nowadays mature technologies fully developed for robust large scale analysis, we show that for emotion recognition the quantity of labelled data is scarce. There is no agreement on clearly defined output spaces and the representations are shallow and largely based on naive geometrical representations

Analyse et synthèse de mouvements théâtraux expressifs

This thesis addresses the analysis and generation of expressive movements for virtual human character. Based on previous results from three different research areas (perception of emotions and biological motion, automatic recognition of affect and computer character animation), a low-dimensional motion representation is proposed. This representation consists of the spatio-temporal trajectories of end-effectors (i.e., head, hands and feet), and pelvis. We have argued that this representation is both suitable and sufficient for characterizing the underlying expressive content in human motion, and for controlling the generation of expressive whole-body movements. In order to prove these claims, this thesis proposes: (i) A new motion capture database inspired by physical theory, which contains three categories of motion (locomotion, theatrical and improvised movements), has been built for several actors; (ii) An automatic classification framework has been designed to qualitatively and quantitatively assess the amount of emotion contained in the data. It has been shown that the proposed low-dimensional representation preserves most of the motion cues salient to the expression of affect and emotions; (iii) A motion generation system has been implemented, both for reconstructing whole-body movements from the low-dimensional representation, and for producing novel end-effector expressive trajectories. A quantitative and qualitative evaluation of the generated whole body motions shows that these motions are as expressive as the movements recorded from human actors.Cette thèse porte sur l'analyse et la génération de mouvements expressifs pour des personnages humains virtuels. Sur la base de résultats de l’état de l’art issus de trois domaines de recherche différents - la perception des émotions et du mouvement biologique, la reconnaissance automatique des émotions et l'animation de personnages virtuels - une représentation en faible dimension des mouvements constituée des trajectoires spatio-temporelles des extrémités des chaînes articulées (tête, mains et pieds) et du pelvis a été proposée. Nous avons soutenu que cette représentation est à la fois appropriée et suffisante pour caractériser le contenu expressif du mouvement humain et pour contrôler la génération de mouvements corporels expressifs. Pour étayer cette affirmation, cette thèse propose:i) une nouvelle base de données de capture de mouvements inspirée par la théorie du théâtre physique. Cette base de données contient des exemples de différentes catégories de mouvements (c'est-à-dire des mouvements périodiques, des mouvements fonctionnels, des mouvements spontanés et des séquences de mouvements théâtraux), produits avec des états émotionnels distincts (joie, tristesse, détente, stress et neutre) et interprétés par plusieurs acteurs.ii) Une étude perceptuelle et une approche basée classification automatique conçus pour évaluer qualitativement et quantitativement l'information liée aux émotions véhiculées et encodées dans la représentation proposée. Nous avons observé que, bien que de légères différences dans la performance aient été trouvées par rapport à la situation où le corps entier a été utilisé, notre représentation conserve la plupart des marqueurs de mouvement liés à l'expression de laffect et des émotions.iii) Un système de synthèse de mouvement capable : a) de reconstruire des mouvements du corps entier à partir de la représentation à faible dimension proposée et b) de produire de nouvelles trajectoires extrémités expressives (incluant la trajectoire du bassin). Une évaluation quantitative et qualitative des mouvements du corps entier générés montre que ces mouvements sont aussi expressifs que les mouvements enregistrés à partir d'acteurs humains

A Comprehensive Review of Data-Driven Co-Speech Gesture Generation

Gestures that accompany speech are an essential part of natural and efficient embodied human communication. The automatic generation of such co-speech gestures is a long-standing problem in computer animation and is considered an enabling technology in film, games, virtual social spaces, and for interaction with social robots. The problem is made challenging by the idiosyncratic and non-periodic nature of human co-speech gesture motion, and by the great diversity of communicative functions that gestures encompass. Gesture generation has seen surging interest recently, owing to the emergence of more and larger datasets of human gesture motion, combined with strides in deep-learning-based generative models, that benefit from the growing availability of data. This review article summarizes co-speech gesture generation research, with a particular focus on deep generative models. First, we articulate the theory describing human gesticulation and how it complements speech. Next, we briefly discuss rule-based and classical statistical gesture synthesis, before delving into deep learning approaches. We employ the choice of input modalities as an organizing principle, examining systems that generate gestures from audio, text, and non-linguistic input. We also chronicle the evolution of the related training data sets in terms of size, diversity, motion quality, and collection method. Finally, we identify key research challenges in gesture generation, including data availability and quality; producing human-like motion; grounding the gesture in the co-occurring speech in interaction with other speakers, and in the environment; performing gesture evaluation; and integration of gesture synthesis into applications. We highlight recent approaches to tackling the various key challenges, as well as the limitations of these approaches, and point toward areas of future development.Comment: Accepted for EUROGRAPHICS 202

Expressive movement generation with machine learning

Movement is an essential aspect of our lives. Not only do we move to interact with our physical environment, but we also express ourselves and communicate with others through our movements. In an increasingly computerized world where various technologies and devices surround us, our movements are essential parts of our interaction with and consumption of computational devices and artifacts. In this context, incorporating an understanding of our movements within the design of the technologies surrounding us can significantly improve our daily experiences. This need has given rise to the field of movement computing – developing computational models of movement that can perceive, manipulate, and generate movements. In this thesis, we contribute to the field of movement computing by building machine-learning-based solutions for automatic movement generation. In particular, we focus on using machine learning techniques and motion capture data to create controllable, generative movement models. We also contribute to the field by creating datasets, tools, and libraries that we have developed during our research. We start our research by reviewing the works on building automatic movement generation systems using machine learning techniques and motion capture data. Our review covers background topics such as high-level movement characterization, training data, features representation, machine learning models, and evaluation methods. Building on our literature review, we present WalkNet, an interactive agent walking movement controller based on neural networks. The expressivity of virtual, animated agents plays an essential role in their believability. Therefore, WalkNet integrates controlling the expressive qualities of movement with the goal-oriented behaviour of an animated virtual agent. It allows us to control the generation based on the valence and arousal levels of affect, the movement’s walking direction, and the mover’s movement signature in real-time. Following WalkNet, we look at controlling movement generation using more complex stimuli such as music represented by audio signals (i.e., non-symbolic music). Music-driven dance generation involves a highly non-linear mapping between temporally dense stimuli (i.e., the audio signal) and movements, which renders a more challenging modelling movement problem. To this end, we present GrooveNet, a real-time machine learning model for music-driven dance generation