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/.

翻译：人类具有预测他人行为并据此规划自身行为的卓越能力。本研究旨在通过解决社会运动预测问题来复现这种能力。我们提出了一个新的基准数据集、一种新颖的问题形式化描述以及一个受认知启发的框架。我们推出Wusi——一个针对团队运动场景的三维多人体运动数据集，该数据集具有高强度策略性人际交互和多样化姿态分布的特征。通过从多智能体强化学习视角重新定义该问题，我们融合了行为克隆与生成对抗模仿学习以提升学习效率与泛化能力。在此基础上，我们进一步考虑了人类社交行动规划过程的认知机制，构建了认知层级框架来预测策略性人际交互。通过全面实验验证了所提数据集与方法的有效性。代码与数据详见 https://walter0807.github.io/Social-CH/。