In this paper, we address the problem of human trajectory forecasting, which aims to predict the inherently multi-modal future movements of humans based on their past trajectories and other contextual cues. We propose a novel motion prediction conditional flow matching model, termed MoFlow, to predict K-shot future trajectories for all agents in a given scene. We design a novel flow matching loss function that not only ensures at least one of the $K$ sets of future trajectories is accurate but also encourages all $K$ sets of future trajectories to be diverse and plausible. Furthermore, by leveraging the implicit maximum likelihood estimation (IMLE), we propose a novel distillation method for flow models that only requires samples from the teacher model. Extensive experiments on the real-world datasets, including SportVU NBA games, ETH-UCY, and SDD, demonstrate that both our teacher flow model and the IMLE-distilled student model achieve state-of-the-art performance. These models can generate diverse trajectories that are physically and socially plausible. Moreover, our one-step student model is $\textbf{100}$ times faster than the teacher flow model during sampling. The code, model, and data are available at our project page: https://moflow-imle.github.io

本研究解决了人类轨迹预测的问题，旨在基于过去的轨迹和上下文线索预测多模态未来运动。我们提出了MoFlow模型，通过一种新颖的流匹配损失函数，实现准确且多样的轨迹预测，同时利用隐式最大似然估计的蒸馏方法优化模型性能。实验结果表明，我们的模型在真实场景中表现出色，尤其是在生成多样且合理的轨迹方面显著提升，且采样速度比教师模型快100倍。

MoFlow：通过隐式最大似然估计蒸馏进行人类轨迹预测的一步流匹配