ExPhy: A Benchmark for Explicit Physical Property Learning in Multi-Object Trajectory Forecasting
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 ...
| Authors | Rui Wang, Yeteng Wu, Xianlin Zhang et al. |
|---|---|
| Primary category | cs.AI |
| Published | 20 August 2026 |
| Last updated at source | 25 August 2026 |
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