AI 中文总结
本研究提出基于真实世界的GAUGE基准，联合评估数值仿真器与生成式视频世界模型的物理保真度，发现无统一保真的物理引擎，视频模型存在轨迹形式正确但物理量错误的问题，为开发高保真仿真器奠定基础。
AI 中文摘要
物理引擎为具身智能提供了大规模训练与评估的支撑，而生成式视频世界模型正作为未来状态与交互的隐式仿真器兴起。然而，现有对物理保真度的评估往往孤立开展，且严重依赖感知相似度或人类判断，难以明确哪些物理原理或参数被违反。我们提出GAUGE，一个基于真实世界的诊断基准，用于联合评估数值仿真器与生成式视频世界模型在重现或偏离真实世界物理规律方面的表现。该基准包含22个受控任务族，涵盖刚体、柔性线缆、织物及体积可变形物体。这些任务基于真实世界轨迹，并搭配校准后的物理元数据、不确定性注释及任务特定可观测指标，覆盖碰撞、摩擦、动量传递、振荡、自接触、变形等基础物理过程，涉及多种材料与条件。我们使用广义轨迹误差在14个任务族上对Isaac Sim、Genesis和Newton进行基准测试，并在5个刚体任务上通过测试物理定律一致性与推断参数的时间稳定性，评估6种图像到视频模型。结果显示不存在统一保真的物理引擎，最大差异出现在脉冲接触、快速织物运动及体积变形场景。我们进一步发现，视频世界模型可生成具有预期方程形式的轨迹，却会恢复错误的加速度、动量传递及振荡时机。GAUGE为开发更具物理保真度的具身智能仿真器与世界模型奠定了基础。
英文摘要
Physics engines facilitate large-scale training and evaluation for embodied intelligence, while generative video world models are emerging as implicit simulators of future states and interactions. However, existing evaluations of physical fidelity are often conducted in isolation and rely heavily on perceptual similarity or human judgments, providing limited insight into which physical principles or parameters are violated. We introduce GAUGE, a real-world-grounded diagnostic benchmark for jointly evaluating how numerical simulators and generative video world models reproduce or deviate from real-world physics. It comprises 22 controlled task families covering rigid bodies, flexible cables, textiles, and volumetric deformable objects. Grounded in real-world trajectories and paired with calibrated physical metadata, uncertainty annotations, and task-specific observables, these tasks cover fundamental physical processes including collision, friction, momentum transfer, oscillation, self-contact, and deformation across diverse materials and conditions. We benchmark Isaac Sim, Genesis, and Newton on 14 task families using generalized trajectory errors, and evaluate 6 image-to-video models on 5 rigid-body tasks by testing physical-law consistency and the temporal stability of inferred parameters. Our results reveal no uniformly faithful physics engine, with the largest discrepancies arising in impulsive contact, rapid textile motion, and volumetric deformation. We further find that video world models can produce trajectories with the expected equation form while recovering incorrect accelerations, momentum transfer, and oscillation timing. GAUGE lays the groundwork for developing more physically faithful simulators and world models for embodied intelligence.
