We describe an implemented system which automatically generates and animates conversations between multiple human-like agents with appropriate and synchronized speech, intonation, facial expressions, and hand gestures. Conversations are created by a dialogue planner that produces the text as well as the intonation of the utterances. The speaker/listener relationship, the text, and the intonation in turn drive facial expressions, lip motions, eye gaze, head motion, and arm gesture generators. Coordinated arm, wrist, and hand motions are invoked to create semantically meaningful gestures. Throughout, we will use examples from an actual synthesized, fully animated conversation. 1

This paper reports results from a program that produces high quality animation of facial expressions and head movements as automatically as possible in conjunction with meaning-based speech synthesis, including spoken intonation. The goal of the research is as much to test and define our theories of the formal semantics for such gestures, as to produce convincing animation. Towards this end we have produced a high level programming language for 3D animation of facial expressions. We have been concerned primarily with expressions conveying information correlated with the intonation of the voice: this includes the differences of timing, pitch, and emphasis that are related to such semantic distinctions of discourse as “focus”, “topic ” and “comment”, “theme ” and “rheme”, or “given ” and “new ” information. We are also interested in the relation of affect or emotion to facial expression. Until now, systems have not embodied such rule-governed translation from spoken utterance meaning to facial expressions. Our system embodies rules that describe and coordinate these relations: intonation/information, intonation/affect and facial expressions/affect. A meaning representation includes discourse information: what is contrastive/background information in the given context, and what is the “topic ” or “theme ” of the discourse. The system maps the meaning representation into how accents and their placement are chosen, how they are conveyed over facial expression and how speech and facial expressions are coordinated. This determines a sequence of functional groups: lip shapes, conversational signals, punctuators, regulators or manipulators. Our algorithms then impose synchrony, create coarticulation effects, and determine affectual signals, eye and head movements. The lowest level representation is the Facial Action Coding System (FACS), which makes the generation system portable to other facial models.

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We present a model of dialogue for embodied virtual agents that can communicate with multiple (human and virtual) agents in a multi-modal setting, including face-to-face spoken and nonverbal, as well as radio interaction, spanning multiple conversations in support of an extended complex task.

We investigate the verbal and nonverbal means for grounding, and propose a design for embodied conversational agents that relies on both kinds of signals to establish common ground in human-computer interaction. We analyzed eye gaze, head nods and attentional focus in the context of a direction-giving task. The distribution of nonverbal behaviors differed depending on the type of dialogue move being grounded, and the overall pattern reflected a monitoring of lack of negative feedback. Based on these results, we present an ECA that uses verbal and nonverbal grounding acts to update dialogue state.

Only humans communicate using language and carry on conversations with one another. And the skills of conversation have developed in humans in such a way as to exploit all of the unique affordances of the human body. We make complex representational gestures with our prehensile hands, gaze away and towards one another out of the corners of our centrally set eyes, and use the pitch and melody of our voices to emphasize and clarify what we are saying. Perhaps because conversation is so defining of humanness and human interaction, the metaphor of face-to-face conversation has been applied to human-computer interface design for quite some time. One of the early arguments for the utility of this metaphor gave a list of features of face-to-face conversation that could be applied fruitfully to human-computer interaction, including mixed initiative, nonverbal communication, sense of presence, rules for transfer of control (Nickerson 1976). However, although these feature

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We are working toward animated agents that can collaborate with human students in virtual worlds. The agent's objective is to help students learn to perform physical, procedural tasks, such as operating and maintaining equipment. Like most of the previous research on task-oriented dialogues, the agent (computer) serves as an expert that can provide guidance to a human novice. Research on such dialogues dates back more than twenty years (Deutsch 1974), and the subject remains an active research area (Allen et al. 1996; Lochbaum 1994; Walker 1996). However, most of that research has focused solely on verbal dialogues, even though the earliest studies clearly showed the ubiquity of nonverbal communication in human task-oriented dialogues (Deutsch 1974). To allow a wider variety of interactions among agents and human students, we use virtual reality (Durlach and Mavor 1995); agents and students cohabit a threedimensional, interactive, simulated mock-up of the student'

In this paper, we discuss why, in designing multiparty mediated systems, we should focus first on providing non-verbal cues which are less redundantly coded in speech than those normally conveyed by video. We show how conveying one such cue, gaze direction, may solve two problems in multiparty mediated communication and collaboration: knowing who is talking to whom, and who is talking about what. As a candidate solution, we present the GAZE Groupware System, which combines support for gaze awareness in multiparty mediated communication and collaboration with small and linear bandwidth requirements. The system uses an advanced, deskmounted eyetracker to metaphorically convey gaze awareness in a 3D virtual meeting room and within shared documents. KEYWORDS: CSCW, multiparty videoconferencing, awareness, attention, gaze direction, eyetracking, VRML 2. INTRODUCTION With recent advances in network infrastructure and computing power, desktop video conferencing and groupware systems are ra...