SGToolkit: An Interactive Gesture Authoring Toolkit for Embodied Conversational Agents

Non-verbal behavior is essential for embodied agents like social robots, virtual avatars, and digital humans. Existing behavior authoring approaches including keyframe animation and motion capture are too expensive to use when there are numerous utterances requiring gestures. Automatic generation me...

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Bibliographic Details
Published in:arXiv.org 2021-08
Main Authors: Yoon, Youngwoo, Park, Keunwoo, Jang, Minsu, Kim, Jaehong, Lee, Geehyuk
Format: Article
Language:English
Subjects:
Animation
Authoring
Avatars
Gesture recognition
Motion capture
Speech
Toolkits
Virtual reality
Online Access:Get full text
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Summary:Non-verbal behavior is essential for embodied agents like social robots, virtual avatars, and digital humans. Existing behavior authoring approaches including keyframe animation and motion capture are too expensive to use when there are numerous utterances requiring gestures. Automatic generation methods show promising results, but their output quality is not satisfactory yet, and it is hard to modify outputs as a gesture designer wants. We introduce a new gesture generation toolkit, named SGToolkit, which gives a higher quality output than automatic methods and is more efficient than manual authoring. For the toolkit, we propose a neural generative model that synthesizes gestures from speech and accommodates fine-level pose controls and coarse-level style controls from users. The user study with 24 participants showed that the toolkit is favorable over manual authoring, and the generated gestures were also human-like and appropriate to input speech. The SGToolkit is platform agnostic, and the code is available at https://github.com/ai4r/SGToolkit.
ISSN:2331-8422
DOI:10.48550/arxiv.2108.04636