The proposed global-to-local generation approach can be extended to handle multi-agent trajectory prediction scenarios by incorporating interactions and dependencies among multiple agents. This extension would involve modifying the key step generation process to consider the trajectories of all agents simultaneously. Each agent's trajectory can be treated as a separate entity, and the global key steps can be generated to cover the future time range for all agents. Subsequently, the local intermediate steps between the key steps can be recursively filled in for each agent, taking into account the spatial and temporal constraints among all agents. By considering the interactions between agents, the model can predict more realistic and feasible trajectories in complex multi-agent scenarios.