license: apache-2.0
paper_ids:
- '2602.11389'
pipeline_tag: image-feature-extraction
datasets:
- clevrer
- pusht
tags:
- object-centric
- world-models
- causal-inference
- jepa
- representation-learning
- vision
C-JEPA: Causal-JEPA
This repository contains the weights and code for Causal-JEPA (C-JEPA), a simple and flexible object-centric world model architecture presented in the paper Causal-JEPA: Learning World Models through Object-Level Latent Interventions.
- Paper: Causal-JEPA: Learning World Models through Object-Level Latent Interventions
- Project Page: https://hazel-heejeong-nam.github.io/cjepa/
- Code: https://github.com/galilai-group/cjepa
Summary
World models require robust relational understanding to support prediction, reasoning, and control. While object-centric representations provide a useful abstraction, they are not sufficient to capture interaction-dependent dynamics. C-JEPA is a simple and flexible object-centric world model that extends masked joint embedding prediction from image patches to object-centric representations. By applying object-level masking that requires an object's state to be inferred from other objects, C-JEPA induces latent interventions with counterfactual-like effects and prevents shortcut solutions, making interaction reasoning essential.
Empirically, C-JEPA demonstrates:
- Improved Visual Reasoning: Consistent gains in visual question answering, with an absolute improvement of about 20% in counterfactual reasoning compared to the same architecture without object-level masking on benchmarks like CLEVRER.
- Efficient Planning: Substantially more efficient planning in agent control tasks (e.g., Push-T), using only 1% of the total latent input features required by patch-based world models while achieving comparable performance.
- Causal Inductive Bias: A formal analysis demonstrates that object-level masking induces a causal inductive bias via latent interventions.
Architecture
Setup and Usage
C-JEPA relies on object-centric encoders (like VideoSAUR or SAVi) to extract representations. For detailed environment setup, dataset preparation, and training/evaluation scripts, please refer to the official GitHub repository. The repository also provides model checkpoints and pre-extracted slot representations for various configurations.
Citation
If you find this work useful, please consider citing:
@article{nam2026causal,
title={Causal-JEPA: Learning World Models through Object-Level Latent Interventions},
author={Nam, Heejeong and Le Lidec, Quentin and Maes, Lucas and LeCun, Yann and Balestriero, Randall},
journal={arXiv preprint arXiv:2602.11389},
year={2026}
}