Abstract
Human trajectory forecasting is important for intelligent multimedia systems operating in visually complex environments, such as autonomous driving and crowd surveillance. Although Conditional Flow Matching (CFM) has shown strong ability in modeling trajectory distributions from spatio-temporal observations, existing approaches still focus primarily on supervised fitting, which may leave social norms and scene constraints insufficiently reflected in generated trajectories. To address this issue, we propose TIGFlow-GRPO, a two-stage generative framework that aligns flow-based trajectory generation with behavioral rules. In the first stage, we build a CFM-based predictor with a Trajectory-Interaction-Graph (TIG) module to model fine-grained visual-spatial interactions and strengthen context encoding. This stage captures both agent-agent and agent-scene relations more effectively, providing more informative conditional features for subsequent alignment. In the second stage, we perform Flow-GRPO post-training,where deterministic flow rollout is reformulated as stochastic ODE-to-SDE sampling to enable trajectory exploration, and a composite reward combines view-aware social compliance with map-aware physical feasibility. By evaluating trajectories explored through SDE rollout, GRPO progressively steers multimodal predictions toward behaviorally plausible futures. Experiments on the ETH/UCY and SDD datasets show that TIGFlow-GRPO improves forecasting accuracy and long-horizon stability while generating trajectories that are more socially compliant and physically feasible. These results suggest that the proposed framework provides an effective way to connect flow-based trajectory modeling with behavior-aware alignment in dynamic multimedia environments.
Abstract (translated)
人类轨迹预测对于在视觉复杂环境中运行的智能多媒体系统（如自动驾驶和人群监控）至关重要。尽管条件流匹配（CFM）已展现出从时空观测中建模轨迹分布的强大能力，但现有方法仍主要侧重于监督拟合，可能导致生成轨迹未能充分反映社会规范与场景约束。针对此问题，我们提出TIGFlow-GRPO——一个将基于流的轨迹生成与行为规则对齐的两阶段生成框架。第一阶段构建基于CFM的预测器，并引入轨迹交互图（TIG）模块以建模细粒度视觉-空间交互并强化上下文编码，该阶段能更有效地捕捉智能体间及智能体与场景的关系，为后续对齐提供信息量更丰富的条件特征。第二阶段执行流式GRPO后训练，其中将确定性流展开重构为随机ODE到SDE采样以实现轨迹探索，并通过组合奖励将视角感知的社会合规性与地图感知的物理可行性相结合。通过评估SDE展开探索的轨迹，GRPO逐步将多模态预测引导至行为合理的未来。在ETH/UCY和SDD数据集上的实验表明，TIGFlow-GRPO在提升预测精度与长时稳定性的同时，能生成更具社会合规性与物理可行性的轨迹。这些结果说明，所提框架为动态多媒体环境中基于流的轨迹建模与行为感知对齐提供了有效连接方式。
URL
https://arxiv.org/abs/2603.24936
