| --- |
| license: cc-by-nc-4.0 |
| pretty_name: FAR datasets |
| task_categories: |
| - image-to-video |
| 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](https://arxiv.org/abs/2609.34677) |
| - 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): |
|
|
| ```bash |
| 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: |
|
|
| ```bash |
| 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](https://ai2thor.allenai.org/) |
| (Apache-2.0) renders and are released under **CC BY-NC 4.0**, like the code. |
|
|
| ## Citation |
|
|
| ```bibtex |
| @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} |
| } |
| ``` |
