GestureBot

Abstract

Embodied conversational agents (ECAs) benefit from non-verbal behavior for natural and efficient interaction with users. Gesticulation – hand and arm movements accompanying speech – is an essential part of non-verbal behavior. Gesture generation models have been developed for several decades: starting with rule-based and ending with mainly data-driven methods. To date, recent end-to-end gesture generation methods have not been evaluated in a real-time interaction with users. We present a proof-of-concept framework, which is intended to facilitate evaluation of modern gesture generation models in interaction.

We demonstrate an extensible open-source framework that contains three components: 1) a 3D interactive agent; 2) a chatbot backend; 3) a gesticulating system. Each component can be replaced, making the proposed framework applicable for investigating the effect of different gesturing models in real-time interactions with different communication modalities, chatbot backends, or different agent appearances.

Implementation

We provide two demonstrations in our github repository. For more information, please refer to the paper.

Rajmund Nagy
Rajmund Nagy
Machine Learning Engineer

Last year Master’s student in machine learning at KTH. My research interests include generative modelling, explainable AI and computer vision.