Semantics for virtual humans

Population of Virtual Worlds with Virtual Humans is increasing rapidly by people who want to create a virtual life parallel to the real one (i.e. Second Life). The evolution of technology is smoothly providing the necessary elements to increase realism within these virtual worlds by creating believable Virtual Humans. However, creating the amount of resources needed to succeed this believability is a difficult task, mainly because of the complexity of the creation process of Virtual Humans. Even though there are many existing available resources, their reusability is difficult because there is not enough information provided to evaluate if a model contains the desired characteristics to be reused. Additionally, the knowledge involved in the creation of Virtual Humans is not well known, nor well disseminated. There are several different creation techniques, different software components, and several processes to carry out before having a Virtual Human capable of populating a virtual environment. The creation of Virtual Humans involves: a geometrical representation with an internal control structure, the motion synthesis with different animation techniques, higher level controllers and descriptors to simulate human-like behavior such individuality, cognition, interaction capabilities, etc. All these processes require the expertise from different fields of knowledge such as mathematics, artificial intelligence, computer graphics, design, etc. Furthermore, there is neither common framework nor common understanding of how elements involved in the creation, development, and interaction of Virtual Humans features are done. Therefore, there is a need for describing (1) existing resources, (2) Virtual Human's composition and features, (3) a creation pipeline and (4) the different levels/fields of knowledge comprehended. This thesis presents an explicit representation of the Virtual Humans and their features to provide a conceptual framework that will interest to all people involved in the creation and development of these characters. This dissertation focuses in a semantic description of Virtual Humans. The creation of a semantic description involves gathering related knowledge, agreement among experts in the definition of concepts, validation of the ontology design, etc. In this dissertation all these procedures are presented, and an Ontology for Virtual Humans is described in detail together with the validations that conducted to the resulted ontology. The goal of creating such ontology is to promote reusability of existing resources; to create a shared knowledge of the creation and composition of Virtual Humans; and to support new research of the fields involved in the development of believable Virtual Humans and virtual environments. Finally, this thesis presents several developments that aim to demonstrate the ontology usability and reusability. These developments serve particularly to support the research on specialized knowledge of Virtual Humans, the population of virtual environments, and improve the believability of these characters

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

Affective Computing

This book provides an overview of state of the art research in Affective Computing. It presents new ideas, original results and practical experiences in this increasingly important research field. The book consists of 23 chapters categorized into four sections. Since one of the most important means of human communication is facial expression, the first section of this book (Chapters 1 to 7) presents a research on synthesis and recognition of facial expressions. Given that we not only use the face but also body movements to express ourselves, in the second section (Chapters 8 to 11) we present a research on perception and generation of emotional expressions by using full-body motions. The third section of the book (Chapters 12 to 16) presents computational models on emotion, as well as findings from neuroscience research. In the last section of the book (Chapters 17 to 22) we present applications related to affective computing

Example Based Caricature Synthesis

The likeness of a caricature to the original face image is an essential and often overlooked part of caricature production. In this paper we present an example based caricature synthesis technique, consisting of shape exaggeration, relationship exaggeration, and optimization for likeness. Rather than relying on a large training set of caricature face pairs, our shape exaggeration step is based on only one or a small number of examples of facial features. The relationship exaggeration step introduces two definitions which facilitate global facial feature synthesis. The first is the T-Shape rule, which describes the relative relationship between the facial elements in an intuitive manner. The second is the so called proportions, which characterizes the facial features in a proportion form. Finally we introduce a similarity metric as the likeness metric based on the Modified Hausdorff Distance (MHD) which allows us to optimize the configuration of facial elements, maximizing likeness while satisfying a number of constraints. The effectiveness of our algorithm is demonstrated with experimental results

Faces and hands : modeling and animating anatomical and photorealistic models with regard to the communicative competence of virtual humans

In order to be believable, virtual human characters must be able to communicate in a human-like fashion realistically. This dissertation contributes to improving and automating several aspects of virtual conversations. We have proposed techniques to add non-verbal speech-related facial expressions to audiovisual speech, such as head nods for of emphasis. During conversation, humans experience shades of emotions much more frequently than the strong Ekmanian basic emotions. This prompted us to develop a method that interpolates between facial expressions of emotions to create new ones based on an emotion model. In the area of facial modeling, we have presented a system to generate plausible 3D face models from vague mental images. It makes use of a morphable model of faces and exploits correlations among facial features. The hands also play a major role in human communication. Since the basis for every realistic animation of gestures must be a convincing model of the hand, we devised a physics-based anatomical hand model, where a hybrid muscle model drives the animations. The model was used to visualize complex hand movement captured using multi-exposure photography.Um überzeugend zu wirken, müssen virtuelle Figuren auf dieselbe Art wie lebende Menschen kommunizieren können. Diese Dissertation hat das Ziel, verschiedene Aspekte virtueller Unterhaltungen zu verbessern und zu automatisieren. Wir führten eine Technik ein, die es erlaubt, audiovisuelle Sprache durch nichtverbale sprachbezogene Gesichtsausdrücke zu bereichern, wie z.B. Kopfnicken zur Betonung. Während einer Unterhaltung empfinden Menschen weitaus öfter Emotionsnuancen als die ausgeprägten Ekmanschen Basisemotionen. Dies bewog uns, eine Methode zu entwickeln, die Gesichtsausdrücke für neue Emotionen erzeugt, indem sie, ausgehend von einem Emotionsmodell, zwischen bereits bekannten Gesichtsausdrücken interpoliert. Auf dem Gebiet der Gesichtsmodellierung stellten wir ein System vor, um plausible 3D-Gesichtsmodelle aus vagen geistigen Bildern zu erzeugen. Dieses System basiert auf einem Morphable Model von Gesichtern und nutzt Korrelationen zwischen Gesichtszügen aus. Auch die Hände spielen ein große Rolle in der menschlichen Kommunikation. Da der Ausgangspunkt für jede realistische Animation von Gestik ein überzeugendes Handmodell sein muß, entwikkelten wir ein physikbasiertes anatomisches Handmodell, bei dem ein hybrides Muskelmodell die Animationen antreibt. Das Modell wurde verwendet, um komplexe Handbewegungen zu visualisieren, die aus mehrfach belichteten Photographien extrahiert worden waren

Synthesis of listener vocalizations : towards interactive speech synthesis

Spoken and multi-modal dialogue systems start to use listener vocalizations, such as uh-huh and mm-hm, for natural interaction. Generation of listener vocalizations is one of the major objectives of emotionally colored conversational speech synthesis. Success in this endeavor depends on the answers to three questions: Where to synthesize a listener vocalization? What meaning should be conveyed through the synthesized vocalization? And, how to realize an appropriate listener vocalization with the intended meaning? This thesis addresses the latter question. The investigation starts with proposing a three-stage approach: (i) data collection, (ii) annotation, and (iii) realization. The first stage presents a method to collect natural listener vocalizations from German and British English professional actors in a recording studio. In the second stage, we explore a methodology for annotating listener vocalizations -- meaning and behavior (form) annotation. The third stage proposes a realization strategy that uses unit selection and signal modification techniques to generate appropriate listener vocalizations upon user requests. Finally, we evaluate naturalness and appropriateness of synthesized vocalizations using perception studies. The work is implemented in the open source MARY text-to-speech framework, and it is integrated into the SEMAINE project\u27s Sensitive Artificial Listener (SAL) demonstrator.Dialogsysteme nutzen zunehmend Hörer-Vokalisierungen, wie z.B. a-ha oder mm-hm, für natürliche Interaktion. Die Generierung von Hörer-Vokalisierungen ist eines der zentralen Ziele emotional gefärbter, konversationeller Sprachsynthese. Ein Erfolg in diesem Unterfangen hängt von den Antworten auf drei Fragen ab: Wo bzw. wann sollten Vokalisierungen synthetisiert werden? Welche Bedeutung sollte in den synthetisierten Vokalisierungen vermittelt werden? Und wie können angemessene Hörer-Vokalisierungen mit der intendierten Bedeutung realisiert werden? Diese Arbeit widmet sich der letztgenannten Frage. Die Untersuchung erfolgt in drei Schritten: (i) Korpuserstellung; (ii) Annotation; und (iii) Realisierung. Der erste Schritt präsentiert eine Methode zur Sammlung natürlicher Hörer-Vokalisierungen von deutschen und britischen Profi-Schauspielern in einem Tonstudio. Im zweiten Schritt wird eine Methodologie zur Annotation von Hörer-Vokalisierungen erarbeitet, die sowohl Bedeutung als auch Verhalten (Form) umfasst. Der dritte Schritt schlägt ein Realisierungsverfahren vor, die Unit-Selection-Synthese mit Signalmodifikationstechniken kombiniert, um aus Nutzeranfragen angemessene Hörer-Vokalisierungen zu generieren. Schließlich werden Natürlichkeit und Angemessenheit synthetisierter Vokalisierungen mit Hilfe von Hörtests evaluiert. Die Methode wurde im Open-Source-Sprachsynthesesystem MARY implementiert und in den Sensitive Artificial Listener-Demonstrator im Projekt SEMAINE integriert