AI 中文总结
ExPhy基准结合轨迹预测与物理属性评估，提出PhyODE模型在OOD-Initial场景下提升预测性能，揭示轨迹预测与物理属性恢复的关联性。
AI 中文摘要
理解物体动力学不仅需要预测未来轨迹，还需检验模型是否捕捉到支配运动的物理属性。然而现有基准极少将物体层面物理属性作为显式评估目标与轨迹预测结合。为填补这一空白，我们推出ExPhy，这是一个包含24000个模拟物理场景的多目标轨迹预测基准，带有质量、摩擦系数和恢复系数等显式物体层面标签。ExPhy提供观测与未来轨迹，以及针对物理参数的分布内（ID）划分、针对初始状态的两个分布外（OOD）划分（OOD-Parameter、OOD-Initial），用于联合评估轨迹预测与物理属性估计。我们进一步实例化PhyODE，这是一个带有显式属性接口的物理引导模型，可从观测轨迹估计物理属性并将其用于可微分未来展开。在长 horizon OOD-Initial设置下，PhyODE相比最强基线将平均位移误差（ADE）和最终位移误差（FDE）分别降低33.1%和31.0%。在ComPhy上的零样本评估进一步评估跨基准迁移能力。属性层面分析表明，准确的轨迹预测不一定意味着能准确恢复潜在物理属性。代码和数据可在该https URL获取。
英文摘要
Understanding object dynamics requires not only predicting future trajectories but also examining whether a model captures the physical properties that govern motion. However, existing benchmarks rarely expose object-level physical properties as explicit evaluation targets alongside trajectory forecasting. To address this gap, we introduce \emph{ExPhy}, a multi-object trajectory forecasting benchmark containing 24,000 simulated physical scenes with explicit object-level labels for mass, friction, and restitution. ExPhy provides observed and future trajectories together with an in-distribution (ID) split and two out-of-distribution (OOD) splits over physical parameters (OOD-Parameter) and initial states (OOD-Initial) for jointly evaluating trajectory forecasting and physical property estimation. We further instantiate \textsc{PhyODE}, a physics-guided model with an explicit property interface that estimates physical properties from observed trajectories and uses them for differentiable future rollout. On the long-horizon OOD-Initial setting, \textsc{PhyODE} reduces ADE and FDE by 33.1\% and 31.0\%, respectively, compared with the strongest baseline. Zero-shot evaluation on ComPhy further assesses cross-benchmark transfer. Property-level analyses reveal that accurate trajectory forecasting does not necessarily imply accurate recovery of the underlying physical properties. Code and data are available at https://github.com/Zest86/ExPhy.
