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.

中文翻译：

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Speech2Properties2Gestures：作为从语音生成代表性手势的工具的手势属性预测

我们提出了一种新的手势生成框架，旨在允许数据驱动的方法产生语义更丰富的手势。我们的方法首先预测是否做手势，然后预测手势属性。然后将这些属性用作能够提供高质量输出的现代概率手势生成模型的条件。这使该方法能够生成既多样化又具有代表性的手势。