  Humans exhibit a remarkable capacity for anticipating the actions of others
and planning their own actions accordingly. In this study, we strive to
replicate this ability by addressing the social motion prediction problem. We
introduce a new benchmark, a novel formulation, and a cognition-inspired
framework. We present Wusi, a 3D multi-person motion dataset under the context
of team sports, which features intense and strategic human interactions and
diverse pose distributions. By reformulating the problem from a multi-agent
reinforcement learning perspective, we incorporate behavioral cloning and
generative adversarial imitation learning to boost learning efficiency and
generalization. Furthermore, we take into account the cognitive aspects of the
human social action planning process and develop a cognitive hierarchy
framework to predict strategic human social interactions. We conduct
comprehensive experiments to validate the effectiveness of our proposed dataset
and approach. Code and data are available at
https://walter0807.github.io/Social-CH/.
