标题： ObjectForesight：从人类视频中预测未来3D物体轨迹 显示英文标题

标题： ObjectForesight: Predicting Future 3D Object Trajectories from Human Videos

摘要： 人类可以轻松地预测通过交互物体可能的运动或变化——想象一个杯子被提起，一把刀切开，或者一个盖子被关闭。 我们旨在赋予计算系统一种类似的能力，直接从被动的视觉观察中预测合理的未来物体运动。 我们引入了ObjectForesight，这是一个3D物体中心的动力学模型，可以从短时自我视角视频序列中预测刚性物体的未来6-DoF位姿和轨迹。 与传统的在像素或潜在空间中运行的世界或动力学模型不同，ObjectForesight在物体层面以显式3D方式表示世界，从而实现具有几何基础且时间连贯的预测，捕捉物体的可用性和轨迹。 为了大规模训练这样的模型，我们利用分割、网格重建和3D位姿估计方面的最新进展，整理了一个包含200多万个短片段的数据集，带有伪真实3D物体轨迹。 通过大量实验，我们展示了ObjectForesight在准确性、几何一致性以及对未见过的物体和场景的泛化能力方面取得了显著提升，建立了一个可扩展的框架，可以直接从观察中学习物理上合理的、物体中心的动力学模型。 objectforesight.github.io 显示英文摘要

摘要： Humans can effortlessly anticipate how objects might move or change through interaction--imagining a cup being lifted, a knife slicing, or a lid being closed. We aim to endow computational systems with a similar ability to predict plausible future object motions directly from passive visual observation. We introduce ObjectForesight, a 3D object-centric dynamics model that predicts future 6-DoF poses and trajectories of rigid objects from short egocentric video sequences. Unlike conventional world or dynamics models that operate in pixel or latent space, ObjectForesight represents the world explicitly in 3D at the object level, enabling geometrically grounded and temporally coherent predictions that capture object affordances and trajectories. To train such a model at scale, we leverage recent advances in segmentation, mesh reconstruction, and 3D pose estimation to curate a dataset of 2 million plus short clips with pseudo-ground-truth 3D object trajectories. Through extensive experiments, we show that ObjectForesight achieves significant gains in accuracy, geometric consistency, and generalization to unseen objects and scenes, establishing a scalable framework for learning physically grounded, object-centric dynamics models directly from observation. objectforesight.github.io