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metadata 
license: other
license_name: redistributed-with-permission
license_link: https://huggingface.co/datasets/kevinLian/LoopNav
pretty_name: FAR datasets (LoopNav)
tags:
  - world-model
  - navigation
  - minecraft
  - latent
  - loopnav

FAR datasets (LoopNav)

Pre-computed latents of the LoopNav Minecraft loop-navigation benchmark, exactly as used to train and evaluate FAR, a latent-diffusion world model with a learned, action-conditioned retrieval memory.

Please cite LoopNav when you use this data: LoopNav: Benchmarking Spatial Consistency in World Models (Lian, Cai, Liang and Liu, 2025). BibTeX below.

Which LoopNav release this is

These latents were encoded from the LoopNav release as it stood in May 2026. On 2026-09-28 the LoopNav repository was refreshed: the ABA trajectories were re-recorded, some ABCA trajectories were replaced, a TURN subset and official train / val / test splits were added, and the earlier release is no longer downloadable. The clips here therefore differ from the current kevinLian/LoopNav, and the train / test split below is FAR's own, not the new official one. This repository preserves the exact corpus behind the FAR paper's LoopNav results.

Layout

Everything is stored at the path the code expects, relative to the repository root:

loopnav/demo.tar, test-NNN.tar, train-NNN.tar   # tar shards; members are datasets/loopnav/latent/<route>/<world>/<radius>/<clip>.*
results/manifests/loopnav/*.json                 # manifest (loopnav_latent.json; *_split.json carries the split field)
results/indices/loopnav/*.json                   # train / test indices
datasets.json                                    # every shard and file with size, SHA-256 and clip count

Shards are plain uncompressed tar files: tar -xf <shard> -C <repo> puts the clips in place. demo is a subset of test; test and train are disjoint.

Download

With the code checked out (fetches the shards, verifies them and extracts them once):

python scripts/download_release.py --no-models --data demo  --corpus loopnav   # the rollout-demo / figure clips
python scripts/download_release.py --no-models --data test  --corpus loopnav   # the test split
python scripts/download_release.py --no-models --data train --corpus loopnav   # test + train

Contents

19,200 first-person Minecraft clips at 20 fps: 9,600 ABA (A→B→A) and 9,600 ABCA (A→B→C→A) out-and-back routes in village worlds of six biomes (desert, plains, savanna, snowy, taiga, zombie), with navigation-range tiers of 5, 15, 30 and 50 blocks. FAR's split: 15,360 train and 3,840 test clips.

Tier Clips Shards Size
demo (the 782 rollout-demo / figure clips) 782 loopnav/demo.tar (1) 25.0 GB
test (test split) 3,840 loopnav/test-000.tar, loopnav/test-001.tar .. (5) 94.5 GB
train (train split) 15,360 loopnav/train-000.tar, loopnav/train-001.tar .. (19) 379.4 GB

Per clip (datasets/loopnav/latent/<route>/<world>/<radius>/<stamp>.*):

File Shape Contents
<stamp>.npy (T, 16, 18, 32) float32 latents from the frozen Oasis 500M ViT-VAE (models/oasis_500m_vit_vae.pth), at its tokenizer scale 0.0784
<stamp>.json list of T dicts LoopNav's per-frame log, unchanged: position x, y, z, yaw, pitch, the action record, the goal, frame_count and extra_info (seed, world, route, range)
<stamp>.keys_jepa.npy (T, 256) float32 cached retriever keys used by the FAR arms
<stamp>.keys_longlive.npy (T, 256) float32 cached keys of the LongLive-style retrieval baseline
<stamp>.keys.npy (T, 256) float32 the dataset config's default key set

License and attribution

The LoopNav videos and logs belong to their authors; this derived corpus (latents, keys and the unchanged per-frame logs) is redistributed with the LoopNav authors' permission. Please cite LoopNav whenever you use it, and FAR if you use the latents, keys or split. The content is Minecraft footage (Minecraft is a trademark of Mojang Synergies AB; this corpus is not affiliated with or endorsed by Mojang or Microsoft). The latents were encoded with the Oasis 500M ViT-VAE (open-oasis, MIT license).

Citation 

@article{lian2025loopnav,
  title   = {LoopNav: Benchmarking Spatial Consistency in World Models},
  author  = {Lian, Kewei and Cai, Shaofei and Liang, Yitao and Liu, Anji},
  journal = {arXiv preprint arXiv:2505.22976},
  year    = {2025},
  url     = {https://arxiv.org/abs/2505.22976}
}

@article{kim2026far,
  title   = {Learning What to Recall: Adaptive Multi-Cue Episodic Memory for World Models},
  author  = {Kim, Beomsu and Lai, Chieh-Hsin and Nguyen, Bac and Bar, Amir and Ye, Jong Chul and Mitsufuji, Yuki},
  journal = {arXiv preprint arXiv:2609.34677},
  year    = {2026},
  url     = {https://arxiv.org/abs/2609.34677}
}