facebookresearch/vjepa2

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4527 stars · 556 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-29, confidence not recorded

Official PyTorch codebase and pretrained models for V-JEPA 2, a self-supervised video encoder trained on internet-scale video, plus V-JEPA 2-AC, an action-conditioned latent world model for robot manipulation. It provides training recipes, model weights, and evaluation code for video understanding, prediction, and planning tasks.

Use cases

• extract video features for action recognition
• anticipate human actions from video
• train a self-supervised video encoder
• run a robot manipulation policy zero-shot
• fine-tune video models on downstream tasks
• predict future video latents with a world model

When to choose

• you need state-of-the-art video understanding features in PyTorch
• you want to experiment with self-supervised video pretraining
• you need a video world model for robot planning without task-specific training

When to avoid

• you need a lightweight production video pipeline rather than research models
• you lack GPU resources for large vision transformers
• you need a general-purpose video editing or playback tool

Facets

library · maturity active

machine-learning deep-learning video-processing computer-vision machine-learning deep-learning computer-vision artificial-intelligence robotics python self-supervised-learning video-encoder world-model pytorch research-models robot-manipulation je gpu

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