1202kbs/FAR-Datasets

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 --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}
}