ObjectForesight: Predicting Future 3D Object Trajectories from Human Videos
Rustin Soraki, Homanga Bharadhwaj, Ali Farhadi, Roozbeh Mottaghi

TL;DR
ObjectForesight is a 3D object-centric model that predicts future object trajectories from egocentric videos, enabling more accurate and geometrically consistent motion predictions by leveraging large-scale 3D data and advanced segmentation techniques.
Contribution
We introduce ObjectForesight, a novel 3D dynamics model that predicts future object motions directly from passive videos, using explicit 3D representations and large-scale pseudo-ground-truth data.
Findings
Achieves significant improvements in prediction accuracy and geometric consistency.
Generalizes well to unseen objects and scenes.
Establishes a scalable framework for 3D object trajectory prediction from videos.
Abstract
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…
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Taxonomy
TopicsRobot Manipulation and Learning · Human Pose and Action Recognition · Human Motion and Animation
