PointZero: 3D Point Track Completion for Learning Transferable 3D Dynamics

World models endow perceptual systems with the ability to predict how scenes evolve under interaction. They are most beneficial when trained on diverse volumes of data, to instill a rich prior into downstream applications. Existing methods for capturing 3D world models are typically scene-specific dynamics models, or require robot action labels - which excludes web video data from the training pool. We study 3D point track completion as a pre-trianing objective for learning transferable 3D dynamics without robot data. Given a single RGB-D observation and sparse partial 3D trajectories (tracks), we predict future 3D tracks of all observed points. We show this is a objective provides a rich dynamics prior, without requiring robot action labels. We contribute a diverse dataset of 2.9 million synthetic frames spanning deformable, articulated, and rigid objects, and use it to train PointZero. We show that a flexible and expressive transformer, PointZero, outperforms prior methods on the same data. We demonstrate the utility of our pre-training objective by fine-tuning PointZero for two downstream applications: (1) action-conditioned dynamics and (2) robot manipulation. When fine-tuned to condition on end-effector pose, PointZero outperforms the baselines on a recent action-conditioned 3D dynamics benchmark. When fine-tuned to predict robot actions, PointZero outperforms or matches the baselines on 6/7 simulated and real-world robot manipulation tasks. We furthermore evaluate training PointZero from scratch to isolate the benefits of our proposed architecture from those of our proposed pre-training objective and dataset. We release the dataset, model checkpoints, and full training recipe.