V-JEPA 2 is a self-supervised learning approach for training video encoders on internet-scale video data. It achieves state-of-the-art performance on motion understanding and human action anticipation tasks. The codebase includes V-JEPA 2-AC, a latent action-conditioned world model post-trained from V-JEPA 2 for robot manipulation without environment-specific data collection.