We propose a new framework for gesture generation, aiming to allow data-driven approaches to produce more semantically rich gestures. Our approach first predicts whether to gesture, followed by a prediction of the gesture properties. Those properties are then used as conditioning for a modern probabilistic gesture-generation model capable of high-quality output. This empowers the approach to generate gestures that are both diverse and representational. Follow-ups and more information can be found on the project page: https://svito-zar.github.io/speech2properties2gestures/ .
Cite
@article{arxiv.2106.14736,
title = {Speech2Properties2Gestures: Gesture-Property Prediction as a Tool for Generating Representational Gestures from Speech},
author = {Taras Kucherenko and Rajmund Nagy and Patrik Jonell and Michael Neff and Hedvig Kjellström and Gustav Eje Henter},
journal= {arXiv preprint arXiv:2106.14736},
year = {2021}
}
Comments
Accepted for publication at the ACM International Conference on Intelligent Virtual Agents (IVA 2021)