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🤖 JEPA-WMs Pretrained Models

Github

ArXiv

Meta AI Research, FAIR

This 🤗 HuggingFace repository hosts pretrained JEPA-WM world models.
👉 See the main repository for training code and datasets.

This repository contains pretrained world model checkpoints from the paper "What Drives Success in Physical Planning with Joint-Embedding Predictive World Models?"

Available Models

JEPA-WM Models

Model	Environment	Resolution	Encoder	Pred. Depth
`jepa_wm_droid`	DROID & RoboCasa	256×256	DINOv3 ViT-L/16	12
`jepa_wm_metaworld`	Metaworld	224×224	DINOv2 ViT-S/14	6
`jepa_wm_pusht`	Push-T	224×224	DINOv2 ViT-S/14	6
`jepa_wm_pointmaze`	PointMaze	224×224	DINOv2 ViT-S/14	6
`jepa_wm_wall`	Wall	224×224	DINOv2 ViT-S/14	6

DINO-WM Baseline Models

Model	Environment	Resolution	Encoder	Pred. Depth
`dino_wm_droid`	DROID & RoboCasa	224×224	DINOv2 ViT-S/14	6
`dino_wm_metaworld`	Metaworld	224×224	DINOv2 ViT-S/14	6
`dino_wm_pusht`	Push-T	224×224	DINOv2 ViT-S/14	6
`dino_wm_pointmaze`	PointMaze	224×224	DINOv2 ViT-S/14	6
`dino_wm_wall`	Wall	224×224	DINOv2 ViT-S/14	6

V-JEPA-2-AC Baseline Models

Model	Environment	Resolution	Encoder	Pred. Depth
`vjepa2_ac_droid`	DROID & RoboCasa	256×256	V-JEPA-2 ViT-G/16	24
`vjepa2_ac_oss`	DROID & RoboCasa	256×256	V-JEPA-2 ViT-G/16	24

VM2M Decoder Heads

Model	Encoder	Resolution
`dinov2_vits_224`	DINOv2 ViT-S/14	224×224
`dinov2_vits_224_INet`	DINOv2 ViT-S/14	224×224
`dinov3_vitl_256_INet`	DINOv3 ViT-L/16	256×256
`vjepa2_vitg_256_INet`	V-JEPA-2 ViT-G/16	256×256

Usage

Via PyTorch Hub (Recommended)

python

import torch

# Load JEPA-WM models
model, preprocessor = torch.hub.load('facebookresearch/jepa-wms', 'jepa_wm_droid')
model, preprocessor = torch.hub.load('facebookresearch/jepa-wms', 'jepa_wm_metaworld')

# Load DINO-WM baselines
model, preprocessor = torch.hub.load('facebookresearch/jepa-wms', 'dino_wm_metaworld')

# Load V-JEPA-2-AC baseline
model, preprocessor = torch.hub.load('facebookresearch/jepa-wms', 'vjepa2_ac_droid')

Via Hugging Face Hub

python

from huggingface_hub import hf_hub_download
import torch

# Download a specific checkpoint
checkpoint_path = hf_hub_download(
    repo_id="facebook/jepa-wms",
    filename="jepa_wm_droid.pth.tar"
)

# Load checkpoint (contains 'encoder', 'predictor', and 'heads' state dicts)
checkpoint = torch.load(checkpoint_path, map_location="cpu")
print(checkpoint.keys())  # dict_keys(['encoder', 'predictor', 'heads', 'opt', 'scaler', 'epoch', 'batch_size', 'lr', 'amp'])

Note: This only downloads the weights. To instantiate the full model with the correct architecture and load the weights, we recommend using PyTorch Hub (see above) or cloning the jepa-wms repository and using the training/eval scripts.

Citation

bibtex

@misc{terver2025drivessuccessphysicalplanning,
      title={What Drives Success in Physical Planning with Joint-Embedding Predictive World Models?},
      author={Basile Terver and Tsung-Yen Yang and Jean Ponce and Adrien Bardes and Yann LeCun},
      year={2025},
      eprint={2512.24497},
      archivePrefix={arXiv},
      primaryClass={cs.AI},
      url={https://arxiv.org/abs/2512.24497},
}

License

These models are licensed under CC-BY-NC 4.0.

facebook/jepa-wms