Social Motion Prediction with Cognitive Hierarchies
This work addresses the challenge of predicting strategic human interactions in team sports, which is incremental as it builds on existing methods with a new dataset and cognitive-inspired approach.
The paper tackles the social motion prediction problem by introducing a new 3D multi-person motion dataset called Wusi for team sports and a cognitive hierarchy framework, achieving improved learning efficiency and generalization through behavioral cloning and generative adversarial imitation learning.
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/.