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A Framework for Integrating Gesture Generation Models into Interactive Conversational Agents

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arxiv 2102.12302 v1 pith:HK2UUN2R submitted 2021-02-24 cs.HC cs.GRcs.LG

classification cs.HCcs.GRcs.LG
keywords frameworkgenerationgesturemodelsdifferentinteractionagentagents
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Embodied conversational agents (ECAs) benefit from non-verbal behavior for natural and efficient interaction with users. Gesticulation - hand and arm movements accompanying speech - is an essential part of non-verbal behavior. Gesture generation models have been developed for several decades: starting with rule-based and ending with mainly data-driven methods. To date, recent end-to-end gesture generation methods have not been evaluated in a real-time interaction with users. We present a proof-of-concept framework, which is intended to facilitate evaluation of modern gesture generation models in interaction. We demonstrate an extensible open-source framework that contains three components: 1) a 3D interactive agent; 2) a chatbot backend; 3) a gesticulating system. Each component can be replaced, making the proposed framework applicable for investigating the effect of different gesturing models in real-time interactions with different communication modalities, chatbot backends, or different agent appearances. The code and video are available at the project page https://nagyrajmund.github.io/project/gesturebot.

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  1. Human-like Nonverbal Behavior with MetaHumans in Real-World Interaction Studies: An Architecture Using Generative Methods and Motion Capture

    cs.HC 2025-01 conditional novelty 4.0 of 10

    A distributed MetaHuman-based architecture combines conversational AI, camera-based user detection, and generative plus motion-captured nonverbal behavior for real-world human-agent interaction studies.

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