license: cc-by-nc-4.0
pretty_name: FAR datasets
tags:
- world-model
- navigation
- embodied-ai
- latent
- ai2thor
FAR datasets
Pre-computed latent corpora used to train and evaluate FAR, a latent-diffusion world model with a learned, action-conditioned retrieval memory, and its baselines.
- Paper: Learning What to Recall: Adaptive Multi-Cue Episodic Memory for World Models
- Code: https://github.com/sony/far
- Checkpoints: https://huggingface.co/1202kbs/FAR-Checkpoints
- Project page: https://1202kbs.github.io/FAR-Project-Page/
Layout
Everything is stored at the path the code expects, relative to the repository root:
<corpus>/demo.tar, test-NNN.tar, train-NNN.tar # tar shards; members are datasets/<corpus>/latent/<episode>/...
datasets/<corpus>/latent/latent_params.json # how the latents were computed (VAE, scale, dtype)
results/manifests/<corpus>/*.json # dataset manifest
results/indices/<corpus>/*.json # train / test (/ ctrl / event) indices
datasets.json # every shard and file with size, SHA-256 and episode count
Shards are plain uncompressed tar files, so tar -xf <shard> -C <repo> puts the
episodes in place. demo is a subset of test; test and train are
disjoint. Not every corpus has a demo tier.
Download
With the code checked out (fetches the shards, verifies them and extracts them once):
python scripts/download_release.py --data demo --corpus ai2thor_v3 # the rollout-demo clips, 1.1 GB
python scripts/download_release.py --data test --corpus ai2thor_v3 # the test split + ctrl siblings, 26 GB
python scripts/download_release.py --data train --corpus ai2thor_v3 # test + train, 84 GB
python scripts/download_release.py --data test --corpus ai2thor_dyn # the AI2-THOR-dyn test split, 3.1 GB
python scripts/download_release.py --data train --corpus ai2thor_dyn # test + train, 15 GB
By hand:
hf download 1202kbs/FAR-Datasets-AI2THOR --repo-type dataset --local-dir /tmp/far-data \
--include "ai2thor_v3/demo.tar" "datasets/**" "results/**" "datasets.json"
tar -xf /tmp/far-data/ai2thor_v3/demo.tar -C <repo>
cp -r /tmp/far-data/datasets /tmp/far-data/results <repo>/
AI2-THOR v3
8,125 iTHOR tour episodes (256x256 RGB at 10 fps, egocentric): an exploration leg
with interaction chains at container and surface stations (open / close / pick up /
put), then a deterministic retrace whose reveals show the changed state. Test
episodes come in seed-matched pairs: an _act episode and a _ctrl sibling that
re-opens the same stations without moving anything (moves: []). The two test
indices are positionally aligned (test_ctrl[i] is the sibling of test[i]).
| Tier | Episodes | Shards | Size |
|---|---|---|---|
demo |
228 (114 test clips + their ctrl siblings; the rollout notebook and paper figures) | ai2thor_v3/demo.tar |
1.1 GB |
test |
2,618 (1,309 act + 1,309 ctrl) | ai2thor_v3/test-000.tar, test-001.tar |
25.9 GB |
train |
5,507 | ai2thor_v3/train-000.tar .. train-002.tar |
57.6 GB |
Per episode (datasets/ai2thor_v3/latent/<episode>/):
| File | Shape | Contents |
|---|---|---|
latents.npy |
(T, 4, 32, 32) float16 | SDXL-VAE latents (madebyollin/sdxl-vae-fp16-fix, posterior mode, at the VAE's 0.13025 scale; see latent_params.json) |
poses.npz |
pos (T, 3), quat (T, 4), heading, t, region (T,) |
agent pose per frame in the THOR world frame (the loader converts to the z-up convention) |
actions.npz |
(T,) / (T, 3) / (T, 2) | per-frame action code, dpos, dyaw, dpitch, interaction code, point_uv, openness |
legs.npz |
leg_index, phase (T,) |
which tour leg / phase each frame belongs to |
closures.npz |
i, j, pos_dist, heading_diff, stale |
loop-closure frame pairs (same viewpoint; stale = the scene changed in between) |
objvis.npz |
one (T,) int32 array per station object | visible pixels of each station object per frame |
meta.json |
scene, seed, fps, resolution, n_frames, eval_start, stations, moves, legs, events, QC |
|
latents.change.npy |
(T, 128) uint8 | packed 32x32 latent-grid loss mask, change family (evaluation) |
latents.change_interact.npy |
(T, 128) uint8 | packed 32x32 loss mask, change ∪ interact (training) |
latents.keys_thor_v1.npy |
(T, 256) float32 | cached retriever keys (served through the backend's key_suffix) |
Loss masks were built with scripts/data/build_masks_ai2thor.py, the event index
with scripts/data/build_event_index_ai2thor.py, and the keys with
scripts/world/build_retriever_keys.py (see the code README to rebuild any of them).
AI2-THOR-dyn
1,002 door-corridor episodes (256x256 RGB at 10 fps, egocentric) over 7 iTHOR
scenes, for off-screen memory of a second agent. The ego watches a doorway while
a second agent crosses it several times, alternating between the two ends of a
corridor, then walks to the doorway and looks at end A and end B. Which end the
second agent is at can only be recovered from the crossings seen earlier. Split
by seed: 800 train / 202 test episodes, every scene in both splits. There is no
demo tier; the whole test split is 3.1 GB.
| Tier | Episodes | Shards | Size |
|---|---|---|---|
test |
202 | ai2thor_dyn/test-000.tar |
3.1 GB |
train |
800 | ai2thor_dyn/train-000.tar |
11.8 GB |
Per episode (datasets/ai2thor_dyn/latent/<episode>/):
| File | Shape | Contents |
|---|---|---|
latents.npy |
(T, 4, 32, 32) float16 | SDXL-VAE latents, as for AI2-THOR v3 (see latent_params.json) |
poses.npz |
pos (T, 3), quat (T, 4), yaw_deg, horizon_deg, heading, t (T,) |
ego pose per frame in the THOR world frame |
agent2.npz |
x, z, px, state (T,) |
second-agent ground truth: floor position, on-screen pixel count, per-frame state code |
meta.json |
scene, seed, number of crossings, spawn, final end, corridor ends, eval_start, legs, QC |
|
latents.agent.npy |
(T, 128) uint8 | packed 32x32 latent-grid mask of the second agent's silhouette (loss weighting) |
latents.keys_obj_v1.npy |
(T, 256) float32 | cached retriever keys used by the FAR arms |
latents.keys_thor_v1.npy |
(T, 256) float32 | cached retriever keys in the AI2-THOR v3 key space |
There is no actions.npz: the ego never acts on the world. The event index
(ai2thor_dyn_latent_train_events.json) holds the reveal spans used for
reveal-anchored sampling; it was built with
scripts/data/build_event_index_ai2thor.py --spans reveal and the masks with
scripts/data/build_agent_masks_dyn.py.
License
The latents and sidecars are derived from AI2-THOR (Apache-2.0) renders and are released under CC BY-NC 4.0, like the code.
Citation
@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}
}