Towards a Common Framework for Multimodal Generation: The Behavior Markup Language

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Speech gesture generation from the trimodal context of text, audio, and speaker identity (2020, methodology): “Interactive applications, in which the agents interact with humans with various speech utterances in real time, mostly use automatic gesture generation methods. typical automatic generation method is rule-based generation (Cassell et al. 2004; Kopp et al. 2006; Marsella et al. 2013).”
Learning Individual Styles of Conversational Gesture (2019, background): “Subsequent rule-based systems [25] proposed new ways of expressing gestures via annotations.”
SmartBody: behavior realization for embodied conversational agents (2008, methodology): “To help facilitate this goal, SmartBody was designed around the SAIBA framework’s Behavior Markup Language (BML) standard [2].”
Conversational Agents and Natural Language Interaction: Techniques and Effective Practices (2011, methodology): “Once the NVBG selects the appropriate behavior for the text, it then packages this up into a Behavioral Markup Language (BML) (Kopp et al., 2006) structure and sends it to the procedural animation system, SmartBody in the output phase.”
SimSensei kiosk: a virtual human interviewer for healthcare decision support (2014, background): “The final result is a schedule of behaviors, described in the Behavior Markup Language (BML; [20]), that is passed to the character animation system.”