2025 · Meta AI · Advanced
V-JEPA 2: Self-Supervised Video Models for Physical Planning
V-JEPA 2 learns predictive video representations that support visual understanding and zero-shot robot control.
Direct answer
What does V-JEPA 2: Self-Supervised Video Models for Physical Planning contribute?
V-JEPA 2 learns predictive video representations that support visual understanding and zero-shot robot control.
Background
Meta trains V-JEPA 2 from video using self-supervised objectives and shows that adding a small amount of robot interaction data can support planning in physical environments.
Problem
The work addresses a central constraint in World Models: building systems that learn useful representations or actions while remaining general enough to transfer beyond a single demonstration or environment.
Core idea
V-JEPA 2 learns predictive video representations that support visual understanding and zero-shot robot control.
Architecture and method
Meta trains V-JEPA 2 from video using self-supervised objectives and shows that adding a small amount of robot interaction data can support planning in physical environments.
- Self-supervised video world model
- Visual prediction for planning
- Zero-shot robot control demonstrations
Results and impact
It is a strong example of human-scale video understanding becoming useful for robot planning without building every skill from scratch.
Prerequisites
- Self-supervised learning
- Video models
- Planning
Recommended reading order
Read the explanation above, review the related topic pages, then use the primary-source links below to inspect the abstract, figures, experiments, and released implementation.
Primary sources
External links are provided after the context needed to evaluate the work.
Follow-up research
Related papers and concepts
World Models
A compact latent model can let an agent learn behavior inside its own predicted environment.
Dreamer: Reinforcement Learning with Latent Imagination
Dreamer learns long-horizon behavior by propagating value gradients through imagined latent trajectories.
DreamerV3: Mastering Diverse Domains through World Models
DreamerV3 uses robust normalization and objectives to learn across more than 150 tasks with one configuration.
Genie: Generative Interactive Environments
Genie learns controllable interactive environments from unlabeled internet video.
Common questions
Frequently asked questions
What is the main idea of V-JEPA 2: Self-Supervised Video Models for Physical Planning?
V-JEPA 2 learns predictive video representations that support visual understanding and zero-shot robot control.
Why is V-JEPA 2: Self-Supervised Video Models for Physical Planning important?
It is a strong example of human-scale video understanding becoming useful for robot planning without building every skill from scratch.
What should I learn before reading V-JEPA 2: Self-Supervised Video Models for Physical Planning?
Recommended prerequisites are Self-supervised learning, Video models, Planning.