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We present a new research task and a dataset to understand human socialinteractions via computational methods, to ultimately endow machines with theability to encode and decode a broad channel of social signals humans use. Thisresearch direction is essential to make a machine that genuinely communicateswith humans, which we call Social Artificial Intelligence. We first formulatethe "social signal prediction" problem as a way to model the dynamics of socialsignals exchanged among interacting individuals in a data-driven way. We thenpresent a new 3D motion capture dataset to explore this problem, where thebroad spectrum of social signals (3D body, face, and hand motions) are capturedin a triadic social interaction scenario. Baseline approaches to predictspeaking status, social formation, and body gestures of interacting individualsare presented in the defined social prediction framework.

Towards Social Artificial Intelligence: Nonverbal Social Signal Prediction in A Triadic Interaction