Koustuv Sinha

Sergio Arnaud

Abha Gejji

Ada Martin

Francois Robert Hogan

Daniel Dugas

Piotr Bojanowski

Vasil Khalidov

Patrick Labatut

Francisco Massa … (voir 13 de plus)

Marc Szafraniec

K. Krishnakumar

Ying Li

Xiaodong Ma

Franziska Meier

Fair at Meta

Mila - Québec

AI Institute

Polytechnique Montréal

A major challenge for modern AI is to learn to understand the world and learn to act largely by observation. This paper explores a self-supe… (voir plus)rvised approach that combines internet-scale video data with a small amount of interaction data (robot trajectories), to develop models capable of understanding, predicting, and planning in the physical world. We first pre-train an action-free joint-embedding-predictive architecture, V-JEPA 2, on a video and image dataset comprising over 1 million hours of internet video. V-JEPA 2 achieves strong performance on motion understanding (77.3 top-1 accuracy on Something-Something v2) and state-of-the-art performance on human action anticipation (39.7 recall-at-5 on Epic-Kitchens-100) surpassing previous task-specific models. Additionally, after aligning V-JEPA 2 with a large language model, we demonstrate state-of-the-art performance on multiple video question-answering tasks at the 8 billion parameter scale (e.g., 84.0 on PerceptionTest, 76.9 on TempCompass). Finally, we show how self-supervised learning can be applied to robotic planning tasks by post-training a latent action-conditioned world model, V-JEPA 2-AC, using less than 62 hours of unlabeled robot videos from the Droid dataset. We deploy V-JEPA 2-AC zero-shot on Franka arms in two different labs and enable picking and placing of objects using planning with image goals. Notably, this is achieved without collecting any data from the robots in these environments, and without any task-specific training or reward. This work demonstrates how self-supervised learning from web-scale data and a small amount of robot interaction data can yield a world model capable of planning in the physical world.

2025-06-11

ArXiv (prépublication)

V-JEPA 2: Self-Supervised Video Models Enable Understanding, Prediction and Planning

Mahmoud Assran

Adrien Bardes

David Fan

Quentin Garrido

Russell Howes

Mojtaba Komeili

Matthew J. Muckley

Ammar Rizvi

Claire Roberts

Sergio Arnaud

Abha Gejji

Ada Martin

Francois Robert Hogan

Daniel Dugas

Piotr Bojanowski

Vasil Khalidov

Patrick Labatut

Francisco Massa … (voir 13 de plus)

Marc Szafraniec

K. Krishnakumar

Yong Li

Xiaodong Ma

Franziska Meier

Fair at Meta

Mila - Québec

AI Institute

Polytechnique Montréal

A major challenge for modern AI is to learn to understand the world and learn to act largely by observation. This paper explores a self-supe… (voir plus)rvised approach that combines internet-scale video data with a small amount of interaction data (robot trajectories), to develop models capable of understanding, predicting, and planning in the physical world. We first pre-train an action-free joint-embedding-predictive architecture, V-JEPA 2, on a video and image dataset comprising over 1 million hours of internet video. V-JEPA 2 achieves strong performance on motion understanding (77.3 top-1 accuracy on Something-Something v2) and state-of-the-art performance on human action anticipation (39.7 recall-at-5 on Epic-Kitchens-100) surpassing previous task-specific models. Additionally, after aligning V-JEPA 2 with a large language model, we demonstrate state-of-the-art performance on multiple video question-answering tasks at the 8 billion parameter scale (e.g., 84.0 on PerceptionTest, 76.9 on TempCompass). Finally, we show how self-supervised learning can be applied to robotic planning tasks by post-training a latent action-conditioned world model, V-JEPA 2-AC, using less than 62 hours of unlabeled robot videos from the Droid dataset. We deploy V-JEPA 2-AC zero-shot on Franka arms in two different labs and enable picking and placing of objects using planning with image goals. Notably, this is achieved without collecting any data from the robots in these environments, and without any task-specific training or reward. This work demonstrates how self-supervised learning from web-scale data and a small amount of robot interaction data can yield a world model capable of planning in the physical world.

2025-06-11

ArXiv (prépublication)

V-JEPA 2: Self-Supervised Video Models Enable Understanding, Prediction and Planning

Mahmoud Assran

Adrien Bardes

David Fan

Quentin Garrido

Russell Howes

Mojtaba Komeili

Matthew J. Muckley

Ammar Rizvi

Claire Roberts

Sergio Arnaud

Abha Gejji

Ada Martin

Francois Robert Hogan

Daniel Dugas

Piotr Bojanowski

Vasil Khalidov

Patrick Labatut

Francisco Massa … (voir 13 de plus)

Marc Szafraniec

K. Krishnakumar

Ying Li

Xiaodong Ma

Franziska Meier

Fair at Meta

Mila - Québec

AI Institute

Polytechnique Montréal

A major challenge for modern AI is to learn to understand the world and learn to act largely by observation. This paper explores a self-supe… (voir plus)rvised approach that combines internet-scale video data with a small amount of interaction data (robot trajectories), to develop models capable of understanding, predicting, and planning in the physical world. We first pre-train an action-free joint-embedding-predictive architecture, V-JEPA 2, on a video and image dataset comprising over 1 million hours of internet video. V-JEPA 2 achieves strong performance on motion understanding (77.3 top-1 accuracy on Something-Something v2) and state-of-the-art performance on human action anticipation (39.7 recall-at-5 on Epic-Kitchens-100) surpassing previous task-specific models. Additionally, after aligning V-JEPA 2 with a large language model, we demonstrate state-of-the-art performance on multiple video question-answering tasks at the 8 billion parameter scale (e.g., 84.0 on PerceptionTest, 76.9 on TempCompass). Finally, we show how self-supervised learning can be applied to robotic planning tasks by post-training a latent action-conditioned world model, V-JEPA 2-AC, using less than 62 hours of unlabeled robot videos from the Droid dataset. We deploy V-JEPA 2-AC zero-shot on Franka arms in two different labs and enable picking and placing of objects using planning with image goals. Notably, this is achieved without collecting any data from the robots in these environments, and without any task-specific training or reward. This work demonstrates how self-supervised learning from web-scale data and a small amount of robot interaction data can yield a world model capable of planning in the physical world.

2025-06-11

ArXiv (prépublication)

V-JEPA 2: Self-Supervised Video Models Enable Understanding, Prediction and Planning

Mahmoud Assran

Adrien Bardes

David Fan

Quentin Garrido

Russell Howes

Mojtaba Komeili

Matthew J. Muckley

Ammar Rizvi

Claire Roberts

Sergio Arnaud

Abha Gejji

Ada Martin

Francois Robert Hogan

Daniel Dugas

Piotr Bojanowski

Vasil Khalidov

Patrick Labatut

Francisco Massa … (voir 13 de plus)

Marc Szafraniec

K. Krishnakumar

Yong Li

Xiaodong Ma

Franziska Meier

Fair at Meta

Mila - Québec

AI Institute

Polytechnique Montréal

A major challenge for modern AI is to learn to understand the world and learn to act largely by observation. This paper explores a self-supe… (voir plus)rvised approach that combines internet-scale video data with a small amount of interaction data (robot trajectories), to develop models capable of understanding, predicting, and planning in the physical world. We first pre-train an action-free joint-embedding-predictive architecture, V-JEPA 2, on a video and image dataset comprising over 1 million hours of internet video. V-JEPA 2 achieves strong performance on motion understanding (77.3 top-1 accuracy on Something-Something v2) and state-of-the-art performance on human action anticipation (39.7 recall-at-5 on Epic-Kitchens-100) surpassing previous task-specific models. Additionally, after aligning V-JEPA 2 with a large language model, we demonstrate state-of-the-art performance on multiple video question-answering tasks at the 8 billion parameter scale (e.g., 84.0 on PerceptionTest, 76.9 on TempCompass). Finally, we show how self-supervised learning can be applied to robotic planning tasks by post-training a latent action-conditioned world model, V-JEPA 2-AC, using less than 62 hours of unlabeled robot videos from the Droid dataset. We deploy V-JEPA 2-AC zero-shot on Franka arms in two different labs and enable picking and placing of objects using planning with image goals. Notably, this is achieved without collecting any data from the robots in these environments, and without any task-specific training or reward. This work demonstrates how self-supervised learning from web-scale data and a small amount of robot interaction data can yield a world model capable of planning in the physical world.

2025-06-11

ArXiv (prépublication)

Multi-Modal Language Models as Text-to-Image Model Evaluators

Jiahui Chen

Candace Ross

Reyhane Askari Hemmat

Melissa Hall

Michal Drozdzal

Adriana Romero Soriano

2025-05-01

ArXiv (prépublication)

Multi-Modal Language Models as Text-to-Image Model Evaluators

Jiahui Chen

Candace Ross

Reyhane Askari Hemmat

Melissa Hall

Michal Drozdzal

Adriana Romero Soriano

2025-05-01

arXiv (publié)

Scaling Language-Free Visual Representation Learning

David Fan

Shengbang Tong

Jiachen Zhu

Zhuang Liu

Xinlei Chen

Michael Rabbat

Nicolas Ballas

Yann Lecun

Amir Bar

Saining Xie

2025-04-01

ArXiv (prépublication)

Scaling Language-Free Visual Representation Learning

David Fan

Shengbang Tong

Jiachen Zhu

Zhuang Liu

Xinlei Chen

Michael Rabbat

Nicolas Ballas

Yann Lecun

Amir Bar

Saining Xie

2025-04-01

arXiv (publié)

MetaMorph: Multimodal Understanding and Generation via Instruction Tuning

Shengbang Tong

David Fan

Jiachen Zhu

Yunyang Xiong

Xinlei Chen

Michael Rabbat

Yann Lecun

Saining Xie

Zhuang Liu

In this work, we propose Visual-Predictive Instruction Tuning (VPiT) - a simple and effective extension to visual instruction tuning that en… (voir plus)ables a pretrained LLM to quickly morph into an unified autoregressive model capable of generating both text and visual tokens. VPiT teaches an LLM to predict discrete text tokens and continuous visual tokens from any input sequence of image and text data curated in an instruction-following format. Our empirical investigation reveals several intriguing properties of VPiT: (1) visual generation ability emerges as a natural byproduct of improved visual understanding, and can be unlocked efficiently with a small amount of generation data; (2) while we find understanding and generation to be mutually beneficial, understanding data contributes to both capabilities more effectively than generation data. Building upon these findings, we train our MetaMorph model and achieve competitive performance on both visual understanding and generation. In visual generation, MetaMorph can leverage the world knowledge and reasoning abilities gained from LLM pretraining, and overcome common failure modes exhibited by other generation models. Our results suggest that LLMs may have strong"prior"vision capabilities that can be efficiently adapted to both visual understanding and generation with a relatively simple instruction tuning process.

2024-12-18

ArXiv (prépublication)

Controlling Multimodal LLMs via Reward-guided Decoding

Oscar Mañas

Pierluca D'Oro