SmartBody: behavior realization for embodied conversational agents
Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems - Volume 1
The Next Step towards a Function Markup Language
IVA '08 Proceedings of the 8th international conference on Intelligent Virtual Agents
Honest Signals: How They Shape Our World
Honest Signals: How They Shape Our World
Modeling wisdom of crowds using latent mixture of discriminative experts
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies: short papers - Volume 2
Towards a common framework for multimodal generation: the behavior markup language
IVA'06 Proceedings of the 6th international conference on Intelligent Virtual Agents
Nonverbal behavior generator for embodied conversational agents
IVA'06 Proceedings of the 6th international conference on Intelligent Virtual Agents
ACM Transactions on Interactive Intelligent Systems (TiiS) - Special Issue on Affective Interaction in Natural Environments
FAAST: The Flexible Action and Articulated Skeleton Toolkit
VR '11 Proceedings of the 2011 IEEE Virtual Reality Conference
Understanding the nonverbal behavior of socially anxious people during intimate self-disclosure
IVA'12 Proceedings of the 12th international conference on Intelligent Virtual Agents
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Modern virtual agents require knowledge about their environment, the interaction itself, and their interlocutors' behavior in order to be able to show appropriate nonverbal behavior as well as to adapt dialog policies accordingly. Recent achievements in the area of automatic behavior recognition and understanding can provide information about the interactants' multimodal nonverbal behavior and subsequently their affective states. In this paper, we introduce a perception markup language (PML) which is a first step towards a standardized representation of perceived nonverbal behaviors. PML follows several design concepts, namely compatibility and synergy, modeling uncertainty, multiple interpretative layers, and extensibility, in order to maximize its usefulness for the research community. We show how we can successfully integrate PML in a fully automated virtual agent system for healthcare applications.