Abstract: To address the limited explicit modeling of motion structure in existing pedestrian trajectory prediction methods and the poor generalization to unseen compositional motion caused by scarce complex combinations,this paper proposes a compositional motion zero-shot pedestrian trajectory prediction network(CZP-Net).Firstly,the motion encoding module extracts an in-teractive motion representation of the target pedestrian and neighboring pedestrians,capturing the joint influence of individual motion tendency and neighborhood interaction on future trajectories.Secondly,a motion-unit prototype bank learns reusable and composable motion representations,while a prototype dispersion constraint reduces redundancy and improves discrimination among prototypes.Thirdly,compositional motion reasoning module adaptively generates combination weights from the similarity between the interaction representation and the motion prototypes,producing a compositional representation for unseen motion patterns.Finally,a shared motion decoder predicts multimodal future trajectories.The future is modeled as a conditional distribution over combinations of motion units,and the combination weights are optimized under the maximum-entropy principle to provide probabilistic and semantic interpretability.On the zero-shot compositional motion task,CZP-Net reduces average displacement error(ADE) and final displacement error(FDE) by 47.46% and 30.47%,respectively,compared with the strongest baseline.On the long-tailed compositional motion task,ADE and FDE are reduced by 53.41% and 45.08%,respectively.

Key words: Pedestrian trajectory prediction, Zero-shot learning, Motion-unit prototype bank, Compositional motion reasoning, Prototype dispersion constraint, Multimodal prediction