FlowMoP：基于条件流匹配的多人随机运动预测框架
FlowMoP: Stochastic Multi-Person Motion Prediction
一篇多人运动预测论文，用流匹配替代确定性输出，在 3DPW 和 CMU-Mocap 上跑赢了原有基线，还首次在 WorldPose 足球数据集上做了基准。
论文提出 FlowMoP，一种条件流匹配框架，可同时为多个智能体生成多样化未来运动，覆盖全局轨迹与根相对姿态。方法采用 B 样条重参数化压缩生成状态空间并保证时间平滑，配合双流 Transformer 让每个智能体的预测以邻近者观测运动为条件。在 CMU-Mocap (UMPM) 和 3DPW 上取得 state-of-the-art，在关节位置误差、姿态误差和最终位移误差上超过确定性基线。作者还在 WorldPose 足球数据集上建立了首个运动预测基线，显示社会条件化能沿交互受限方向收敛预测不确定性。
FlowMoP: Stochastic Multi-Person Motion Prediction
Predicting the future motion of multiple people is inherently stochastic: the same observed history admits many plausible continuations depending on intent, coordination, and social context. Yet the dominant paradigm in multi-person 3D motion prediction remains deterministic, with existing methods producing a fixed number of outputs per agent. In contrast, we present a conditional flow matching framework that jointly generates diverse future motions for multiple agents, modeling both global trajectories and root-relative poses. A B-spline reparameterization compresses the generative state space while enforcing temporal smoothness, and a dual-stream transformer conditions each agent's predictions on observed neighbor motion for interaction-aware forecasting. Our method achieves state-of-the-art performance on CMU-Mocap (UMPM) and 3DPW, outperforming deterministic baselines on joint position error, pose error, and final displacement error despite producing a full predictive distribution. We additionally establish the first motion prediction baseline on WorldPose, a large-scale professional soccer dataset, where social conditioning demonstrably concentrates predictive uncertainty along interaction-constrained directions.