Towards Social Artificial Intelligence: Nonverbal Social Signal Prediction in a Triadic Interaction
Hanbyul Joo, Tomas Simon, Mina Cikara, Yaser Sheikh; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2019, pp. 10873-10883
Abstract
We present a new research task and a dataset to understand human social interactions via computational methods, to ultimately endow machines with the ability to encode and decode a broad channel of social signals humans use. This research direction is essential to make a machine that genuinely communicates with humans, which we call Social Artificial Intelligence. We first formulate the "social signal prediction" problem as a way to model the dynamics of social signals exchanged among interacting individuals in a data-driven way. We then present a new 3D motion capture dataset to explore this problem, where the broad spectrum of social signals (3D body, face, and hand motions) are captured in a triadic social interaction scenario. Baseline approaches to predict speaking status, social formation, and body gestures of interacting individuals are presented in the defined social prediction framework.
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bibtex]
@InProceedings{Joo_2019_CVPR,
author = {Joo, Hanbyul and Simon, Tomas and Cikara, Mina and Sheikh, Yaser},
title = {Towards Social Artificial Intelligence: Nonverbal Social Signal Prediction in a Triadic Interaction},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2019}
}