Speech2Properties2Gestures: Gesture-Property Prediction as a Tool for Generating Representational Gestures from Speech
This work addresses the need for more expressive gesture generation in human-computer interaction, though it appears incremental as it builds on existing probabilistic models.
The paper tackles the problem of generating semantically rich gestures from speech by predicting gesture properties and using them to condition a probabilistic model, resulting in diverse and representational gestures.
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/ .