Predicting future physical states stands as a central challenge in embodied intelligence. Existing video world models leave 3D dynamics implicit, action-based formulations remain tied to specific robo
WorldFlow is a generative world model that jointly predicts future video and 3D trajectories using continuous displacement tokens and a Mixture-of-Transformers backbone, improving physical dynamics prediction across diverse benchmarks.
Predicting future physical states stands as a central challenge in embodied intelligence. Existing video world models leave 3D dynamics implicit, action-based formulations remain tied to specific robot configurations, and 3D motion prediction models use flow-only supervision, providing a limited learning signal for physical dynamics. To circumvent these limitations, we introduce , a generative world model that jointly predicts future video and query-based 3D trajectories. By parameterizing 3D motion as continuous displacement tokens aligned with the video grid, a unified Mixture-of-Transformers (MoT) backbone jointly denoises both modalities under a rectified-flow objective. This…
Summary and abstract excerpt via Hugging Face Papers ↗ · arXiv.
WorldFlow: Joint Video and 3D Motion Prediction via Large-Scale Pretraining was first seen on Hugging Face · Daily papers on Nov 3, 2026. Since then it has appeared in 10 checks on 2 different days. Its best position was #23 of 30, reached on Nov 3, 11:28 UTC. It dropped off the list after Nov 4, 05:28 UTC.
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#23 on Hugging Face · Daily papers, on Nov 3, 11:28 UTC. Trendoday checks this list every 120 minutes.
It was first seen on Nov 3, 2026 and has been on the list on 2 days (10 checks) since.