GEM-4D: Geometry-Enhanced Video World Models for Robot Manipulation
GEM-4D is a geometry-grounded video world model that improves robot manipulation by injecting dense 4D correspondence supervision distilled from a pretrained geometry foundation model. It jointly captures appearance and geometric structure without additional inference cost. An inverse dynamics module converts consistent video rollouts into executable robot trajectories. GEM-4D achieves state-of-the-art performance on video prediction and geometric consistency, boosting real-world manipulation success from 61% to 81%.
Article intelligence
Key points
- GEM-4D enhances video world models with dense 4D correspondence supervision for geometric consistency.
- It maintains a single-stream architecture with no extra inference cost.
- An inverse dynamics module translates video rollouts into robot trajectories.
- Real-world manipulation success rate improves from 61% to 81%.
Why it matters
This matters because GEM-4D enhances video world models with dense 4D correspondence supervision for geometric consistency.
Technical impact
May affect model selection, inference cost, product capability, and evaluation benchmarks.
[2605.22882] GEM-4D: Geometry-Enhanced Video World Models for Robot Manipulation
[Submitted on 20 May 2026]
Title:GEM-4D: Geometry-Enhanced Video World Models for Robot Manipulation
View a PDF of the paper titled GEM-4D: Geometry-Enhanced Video World Models for Robot Manipulation, by Kaichen Zhou and 10 other authors
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Abstract:Video world models can generate realistic futures from a single instruction, but they often fail to preserve consistent point-level motion over time. As a result, the generated videos appear plausible, yet lack the physical grounding required for reliable action execution, such as robot manipulation. We present GEM-4D, a geometry-grounded video world model that resolves this limitation by injecting dense 4D correspondence supervision, distilled from a pretrained geometry foundation model, into the video generative backbone during training. This supervision enables the model to jointly capture appearance and geometric structure while retaining a single-stream architecture with no additional inference cost. We further introduce an inverse dynamics module that converts correspondence-consistent video rollouts into executable robot trajectories, enabling direct deployment in both real-world and simulated manipulation. GEM-4D achieves state-of-the-art performance on both video prediction and geometric consistency across simulation and realistic scenarios and improves real-world manipulation success from 61% to 81%. Additional results are available at the project page: this https URL.
Comments: Robotic World Model, Video Generative Model
Subjects:
Computer Vision and Pattern Recognition (cs.CV); Robotics (cs.RO)
Cite as: arXiv:2605.22882 [cs.CV]
(or arXiv:2605.22882v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2605.22882
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Kaichen Zhou [view email] [v1] Wed, 20 May 2026 21:36:44 UTC (3,896 KB)
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