手势属性预测作为从语音中生成代表性手势的工具

Proceedings of the 21st ACM International Conference on Intelligent Virtual Agents Pub Date : 2021-06-28 DOI:10.1145/3472306.3478333

Taras Kucherenko, Rajmund Nagy, Patrik Jonell, Michael Neff, Hedvig Kjellstrom, G. Henter

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引用次数: 11

摘要

我们提出了一个新的手势生成框架，旨在允许数据驱动的方法产生更多语义丰富的手势。我们的方法首先预测是否要做手势，然后预测手势的属性。然后将这些属性用作能够产生高质量输出的现代概率手势生成模型的条件。这使得该方法能够生成既多样又具有代表性的手势。后续信息和更多信息可以在项目页面上找到:https://svito-zar.github.io/speech2properties2gestures/

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Speech2Properties2Gestures: Gesture-Property Prediction as a Tool for Generating Representational Gestures from Speech

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/

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Proceedings of the 21st ACM International Conference on Intelligent Virtual Agents

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