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.

本研究提出一种社交信号预测问题及数据集，通过数据驱动的方法对互动个体之间交换的社交信号动态进行建模，为机器赋予与人类交流的能力，称为社交人工智能，通过一个三方社交互动场景的 3D 运动捕捉数据集，提出预测互动个体的语言状态、社交形态和身体姿势的基线方法，并提出一个定义明确的社交预测框架。