  The ability to perceive and reason about social interactions in the context
of physical environments is core to human social intelligence and human-machine
cooperation. However, no prior dataset or benchmark has systematically
evaluated physically grounded perception of complex social interactions that go
beyond short actions, such as high-fiving, or simple group activities, such as
gathering. In this work, we create a dataset of physically-grounded abstract
social events, PHASE, that resemble a wide range of real-life social
interactions by including social concepts such as helping another agent. PHASE
consists of 2D animations of pairs of agents moving in a continuous space
generated procedurally using a physics engine and a hierarchical planner.
Agents have a limited field of view, and can interact with multiple objects, in
an environment that has multiple landmarks and obstacles. Using PHASE, we
design a social recognition task and a social prediction task. PHASE is
validated with human experiments demonstrating that humans perceive rich
interactions in the social events, and that the simulated agents behave
similarly to humans. As a baseline model, we introduce a Bayesian inverse
planning approach, SIMPLE (SIMulation, Planning and Local Estimation), which
outperforms state-of-the-art feed-forward neural networks. We hope that PHASE
can serve as a difficult new challenge for developing new models that can
recognize complex social interactions.
