Add compact SpatialVID-derived navigation videos with source notices and checksums

README.md

@@ -9,95 +9,123 @@ size_categories:
 tags:
 - navigation
 - embodied-ai
+- video
 ---
 
 # ScaleNav Data
 
-Navigation annotations, recorded trajectories, fixed splits and construction
-prompts for **ScaleNav: Scalable Internet Video Pretraining for
-Language-Conditioned Local Navigation**.
+Prepared Internet navigation videos, instructions, recorded simulation
+trajectories and evaluation selections for **ScaleNav: Scalable Internet Video
+Pretraining for Language-Conditioned Local Navigation**.
 
 [Code](https://github.com/yangyihui0502/ScaleNav) ·
 [Model](https://huggingface.co/Yangyihui/ScaleNav) ·
 [Project page](https://yangyihui0502.github.io/scalenav-review/)
 
-## Contents
-
-| Collection | Training | Validation | Files |
-| --- | ---: | ---: | --- |
-| Internet navigation | 18,184 segments | 65 segments | `annotations/internet.jsonl.gz` |
-| Simulated navigation | 2,460 episodes | 140 episodes | `annotations/simulation.jsonl.gz`, `simulation.npz` |
-| Habitat-GS-LN evaluation | 157 tasks in 45 scenes; includes a 10-task test subset in 4 scenes | — | `annotations/evaluation/habitatgs_ln.json` |
-| Short-Horizon OVON evaluation | 524 tasks in 36 scenes | — | `annotations/evaluation/short_horizon_ovon.json` |
-
-The Internet collection references 13,647 upstream clips. Simulation uses
-618 training and 32 validation scenes with no scene overlap. The 45 evaluation
-scenes are disjoint from these 650 scenes. Each training record retains its
-fixed `split` field.
-
-`annotations/manifest.json` pins upstream revisions, counts, smoke-test IDs and
-checksums. Five templates in `annotations/prompts/` describe extraction, review,
-instruction refinement and paraphrasing. Simulation arrays use compressed NumPy
-format without pickle; JSONL records supply their offsets and camera settings.
+## Download and prepare
 
-## Download annotations
-
-This 4.8 MB package contains our annotations and trajectories. From the ScaleNav
-code repository:
+From the ScaleNav code repository, download the small Internet training pack:
 
 ```bash
-hf download Yangyihui/ScaleNav-Data --repo-type dataset --revision v1.2 \
-  --include 'annotations/*' --local-dir .
+python scripts/prepare_data.py internet --subset smoke --split train \
+  --source data/downloads/scalenav --output data/smoke/goto/internet \
+  --instructions goto --download
 ```
 
-The matching code release includes these same annotations, so downloading them
-again is optional. Pass a full commit hash with `--revision` for a fixed version.
-
-Download original videos from
-[SpatialVID-HQ](https://huggingface.co/datasets/FelixYuan-YF/SpatialVID-HQ) and scenes
-from [InteriorGS](https://huggingface.co/datasets/spatialverse/InteriorGS) using your
-own approved upstream access. Follow the
-[data guide](https://github.com/yangyihui0502/ScaleNav/blob/main/docs/DATA.md)
-to combine these source assets with our labels and prepare training inputs locally.
-The [small training subset](https://github.com/yangyihui0502/ScaleNav/blob/main/docs/DATA.md#small-training-subset)
-uses one official SpatialVID shard and one InteriorGS scene (1.48 GB of upstream
-downloads) to prepare 17 Internet segments and four simulated episodes locally.
-The separate
-[evaluation guide](https://github.com/yangyihui0502/ScaleNav/blob/main/docs/EVALUATION.md)
-covers the 10-case navigation test subset and both full test sets. Short-Horizon OVON
-preparation downloads official task sources, joins our fixed 524-task selection
-and builds target footprints and reference paths from locally obtained HM3D
-scenes. The selected scene assets occupy 3.48 GB.
-
-Habitat-GS-LN preparation reconstructs 39 scene visuals from official InteriorGS
-compressed files and checks that they match the benchmark assets byte for byte.
-Those scenes also require their original evaluation navmeshes (0.75 MB total),
-obtained separately from the asset authors under the upstream terms. The pinned
-Habitat-GS collection supplies the other six scenes. All 45 prepared scenes total
-8.59 GB; downloaded source caches are additional.
+The script downloads a fixed release, verifies archive and video SHA-256 hashes,
+extracts the selected videos and writes paired annotations and training configs.
+It preserves the prepared MP4 files without another encoding pass. The default
+path downloads only ScaleNav packs; original SpatialVID archives are optional.
+
+| Selection | Samples | Download |
+| --- | ---: | --- |
+| Small Internet training subset | 17 segments / 14 clips | About 10.6 MB, plus the video index |
+| Internet training | 18,184 segments / 13,605 clips | About 8.6 GB in four independent TAR packs |
+| Internet validation | 65 segments / 42 clips | About 37 MB |
+| Annotation package | Internet/simulation labels, poses and evaluation selections | About 4.8 MB; also included in the code repository |
+
+For all Internet videos, omit `--subset smoke --split train` and use
+`--output data/training/goto/internet`. For validation only, use `--split val`
+and a separate output directory. Packs remain in `--source` for reuse; omit
+`--download` to prepare from an existing local cache. The small training subset
+is part of the training split, not a navigation benchmark.
+
+See the [data guide](https://github.com/yangyihui0502/ScaleNav/blob/main/docs/DATA.md)
+for simulation preparation and latent caching, and the
+[training guide](https://github.com/yangyihui0502/ScaleNav/blob/main/docs/TRAINING.md)
+for the four training phases. The combined small training subset uses the video
+pack above and one approximately 44 MB scene from the official InteriorGS release.
+
+## Contents
+
+- `internet/train-*.tar`, `internet/val.tar`: selected SpatialVID-derived videos
+  at 512×288 / 4 Hz. `internet/train-smoke.tar` is independently downloadable.
+- Each TAR contains `videos/<clip-id>.mp4`, `annotations.jsonl`, `LICENSE` and
+  `SOURCE.txt`. Annotations include both canonical and paraphrased instructions,
+  fixed splits, upstream archive paths and zero-based, end-exclusive frame bounds.
+- `internet/videos.jsonl.gz`: per-video hashes, sizes, frame counts, source
+  identifiers and full-release archive membership.
+- `internet/manifest.json`: pack hashes and the associated annotation checksum.
+- `annotations/internet.jsonl.gz`: 18,249 accepted Internet navigation segments.
+- `annotations/simulation.jsonl.gz`, `simulation.npz`: 2,600 trajectories and
+  their instructions, actions, poses and camera settings. No pickle is required.
+- `annotations/evaluation/`: separate Habitat-GS-LN and Short-Horizon OVON tasks.
+- `annotations/prompts/`: extraction, review, refinement and paraphrasing prompts.
+
+Internet training and validation are disjoint by upstream clip ID. Simulation
+uses 618 training and 32 validation scenes, with no scene overlap; its 650 scenes
+are disjoint from the 45 Habitat-GS-LN evaluation scenes. The included
+`annotations/manifest.json` records fixed sample IDs and upstream revisions.
+
+## Simulation and evaluation
+
+Download InteriorGS scenes from the official source and replay our recorded
+poses to render training video. Simulation scenes, rendered simulation videos
+and latent caches are not included in these Internet video packs.
+
+The [evaluation guide](https://github.com/yangyihui0502/ScaleNav/blob/main/docs/EVALUATION.md)
+provides separate preparation and evaluation commands for:
+
+| Benchmark | Tasks | Scene assets |
+| --- | ---: | --- |
+| Habitat-GS-LN test subset | 10 tasks in 4 scenes | 663 MB |
+| Full Habitat-GS-LN | 157 tasks in 45 scenes | 8.59 GB prepared |
+| Full Short-Horizon OVON | 524 tasks in 36 scenes | 3.48 GB selected HM3D assets |
+
+Full Habitat-GS-LN reconstructs 39 scene visuals from official InteriorGS files
+and requires their original evaluation navmeshes (0.75 MB), obtained separately
+from the asset authors. The pinned Habitat-GS collection supplies the remaining
+six scenes. Short-Horizon OVON preparation downloads official task sources and
+builds target footprints and reference paths from locally obtained HM3D scenes.
+These are closed-loop evaluations; observations are rendered during navigation.
 
 ## Sources and license
 
-The annotations use [CC BY-NC-SA 4.0](LICENSE). Attribute ScaleNav and the relevant
-upstream dataset, identify modifications and retain applicable terms.
-The code has a separate Apache-2.0 license.
-
-- Internet segments reference [SpatialVID-HQ](https://huggingface.co/datasets/FelixYuan-YF/SpatialVID-HQ),
-  whose data uses CC BY-NC-SA 4.0. Obtain footage from the official source.
-- Simulation and Habitat-GS-LN reference [InteriorGS](https://huggingface.co/datasets/spatialverse/InteriorGS).
-  Obtain access and accept its
-  [terms](https://kloudsim-usa-cos.kujiale.com/InteriorGS/InteriorGS_Terms_of_Use.pdf),
-  which limit use to noncommercial research/education and prohibit redistribution
-  of downloaded data.
-
-- Short-Horizon OVON references [HM3D](https://github.com/matterport/habitat-matterport-3dresearch),
-  [HM3D-OVON](https://huggingface.co/datasets/nyokoyama/hm3d_ovon) and
-  [ABot starting poses](https://huggingface.co/datasets/acvlab/ABotN-Short-Horizon-OVON).
-  Obtain their assets separately under their respective terms. This package
-  includes task IDs, rewritten instructions, target instance IDs, preparation
-  settings and checksums; the preparation script derives coordinates and
-  footprints locally.
-
-No upstream videos, scene geometry, copied geometry annotations, rendered RGB
-or latent caches are included. This annotation license does not grant additional
-rights to source assets. Render observations locally using the recorded poses.
+The SpatialVID-derived videos and ScaleNav annotations use
+[CC BY-NC-SA 4.0](LICENSE): attribute the original source and ScaleNav, retain
+license notices, identify changes, use the data noncommercially and share
+adaptations under the same license. Code uses a separate Apache-2.0 license.
+
+- **SpatialVID-HQ**, Jiahao Wang, Yufeng Yuan and collaborators (CVPR 2026):
+  [dataset](https://huggingface.co/datasets/FelixYuan-YF/SpatialVID-HQ),
+  [paper](https://arxiv.org/abs/2509.09676),
+  [code and license declaration](https://github.com/NJU-3DV/SpatialVID#license).
+  ScaleNav selected clips, resampled them to 512×288 / 4 Hz, and
+  added navigation intervals, instructions, paraphrases and fixed splits.
+  See [the source and modification notice](internet/SOURCE.txt).
+- **InteriorGS**, SpatialVerse Research Team / Manycore Tech Inc.:
+  obtain [official access](https://huggingface.co/datasets/spatialverse/InteriorGS)
+  and follow its [terms](https://kloudsim-usa-cos.kujiale.com/InteriorGS/InteriorGS_Terms_of_Use.pdf).
+  Downloaded scene data must not be redistributed under those terms.
+- **HM3D**, [HM3D-OVON](https://huggingface.co/datasets/nyokoyama/hm3d_ovon), and
+  [ABot Short-Horizon OVON](https://huggingface.co/datasets/acvlab/ABotN-Short-Horizon-OVON):
+  obtain source assets under their respective terms, including the
+  [Matterport academic-use agreement](https://matterport.com/legal/matterport-end-user-license-agreement-academic-use-model-data).
+
+No InteriorGS/HM3D scenes, copied geometry annotations or navmeshes are included.
+The Internet videos are an independently prepared subset, not an official
+SpatialVID release or endorsement. Copyright in the original footage remains
+with its respective holders; the license grants only rights its licensors have
+authority to grant. For attribution corrections or removal requests, provide the
+affected clip IDs in this repository's Discussions or the linked code repository's
+Issues. The full license includes the disclaimer of warranties.

SHA256SUMS.json

@@ -1,58 +1,102 @@
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annotations/SPATIALVID_NOTICE.txt

@@ -0,0 +1,33 @@
+ScaleNav Internet Navigation Videos
+
+Source: SpatialVID-HQ, Jiahao Wang, Yufeng Yuan and collaborators, CVPR 2026.
+Paper: https://arxiv.org/abs/2509.09676
+Project: https://github.com/NJU-3DV/SpatialVID
+Dataset: https://huggingface.co/datasets/FelixYuan-YF/SpatialVID-HQ
+Source revision: 97e22f97447c012ef7460e5989b83a674643a880
+
+The SpatialVID-derived videos and ScaleNav annotations are distributed under
+Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International.
+License: https://creativecommons.org/licenses/by-nc-sa/4.0/
+The complete license, including its disclaimer of warranties, is in LICENSE.
+Retain attribution and license notices, identify further changes, use the data
+noncommercially, and distribute adaptations under the same license.
+
+ScaleNav selected clips and navigation intervals, resampled video to 4 Hz and
+512 x 288 pixels, and added navigation instructions, paraphrases,
+and fixed training/validation splits. This release preserves the prepared
+training MP4 files without another video encoding pass. Frame intervals are
+zero-based and end-exclusive on the prepared 4 Hz timeline. Each archive's
+annotations.jsonl retains upstream clip IDs, archive paths and source-video
+paths. The release index additionally records video hashes and frame counts.
+
+Copyright 2026 ScaleNav Authors applies to our original contributions, not to
+the underlying footage. Original rights remain with their respective holders.
+The license grants only rights that the licensors have authority to grant;
+it does not confer additional privacy, publicity or other third-party rights.
+This is an independently prepared subset, not an official SpatialVID release
+or an endorsement by its authors. No InteriorGS or HM3D scene data is included.
+
+For attribution corrections or removal requests, identify the affected clip IDs
+at https://huggingface.co/datasets/Yangyihui/ScaleNav-Data/discussions or
+https://github.com/yangyihui0502/ScaleNav/issues .

internet/LICENSE

@@ -0,0 +1,170 @@
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+
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+
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+
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+
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+Your exercise of the Licensed Rights is expressly made subject to the following conditions.
+
+     a.	Attribution.
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+          1. If You Share the Licensed Material (including in modified form), You must:
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+               C. indicate the Licensed Material is licensed under this Public License, and include the text of, or the URI or hyperlink to, this Public License.
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+
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+For the avoidance of doubt, this Section 4 supplements and does not replace Your obligations under this Public License where the Licensed Rights include other Copyright and Similar Rights.
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+
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+
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+
+Creative Commons may be contacted at creativecommons.org.

internet/SOURCE.txt

@@ -0,0 +1,33 @@
+ScaleNav Internet Navigation Videos
+
+Source: SpatialVID-HQ, Jiahao Wang, Yufeng Yuan and collaborators, CVPR 2026.
+Paper: https://arxiv.org/abs/2509.09676
+Project: https://github.com/NJU-3DV/SpatialVID
+Dataset: https://huggingface.co/datasets/FelixYuan-YF/SpatialVID-HQ
+Source revision: 97e22f97447c012ef7460e5989b83a674643a880
+
+The SpatialVID-derived videos and ScaleNav annotations are distributed under
+Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International.
+License: https://creativecommons.org/licenses/by-nc-sa/4.0/
+The complete license, including its disclaimer of warranties, is in LICENSE.
+Retain attribution and license notices, identify further changes, use the data
+noncommercially, and distribute adaptations under the same license.
+
+ScaleNav selected clips and navigation intervals, resampled video to 4 Hz and
+512 x 288 pixels, and added navigation instructions, paraphrases,
+and fixed training/validation splits. This release preserves the prepared
+training MP4 files without another video encoding pass. Frame intervals are
+zero-based and end-exclusive on the prepared 4 Hz timeline. Each archive's
+annotations.jsonl retains upstream clip IDs, archive paths and source-video
+paths. The release index additionally records video hashes and frame counts.
+
+Copyright 2026 ScaleNav Authors applies to our original contributions, not to
+the underlying footage. Original rights remain with their respective holders.
+The license grants only rights that the licensors have authority to grant;
+it does not confer additional privacy, publicity or other third-party rights.
+This is an independently prepared subset, not an official SpatialVID release
+or an endorsement by its authors. No InteriorGS or HM3D scene data is included.
+
+For attribution corrections or removal requests, identify the affected clip IDs
+at https://huggingface.co/datasets/Yangyihui/ScaleNav-Data/discussions or
+https://github.com/yangyihui0502/ScaleNav/issues .

internet/manifest.json

@@ -0,0 +1,66 @@
+{
+  "version": 1,
+  "repo_id": "Yangyihui/ScaleNav-Data",
+  "annotations_sha256": "60b2881e6bc34a803befb765c554cd8963ddcccf14b091f7f6351d19e6fa3560",
+  "license": "CC-BY-NC-SA-4.0",
+  "upstream": {
+    "repo_id": "FelixYuan-YF/SpatialVID-HQ",
+    "revision": "97e22f97447c012ef7460e5989b83a674643a880"
+  },
+  "index": {
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+    "bytes": 1072212,
+    "sha256": "0736b72d24ed4d8a3ee84e6259f4f7940b811ef59665360f2780bce7e5c3d46c"
+  },
+  "files": [
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+      "bytes": 2151495680,
+      "sha256": "bfe572b2d419c682a057488db9d76aaa3d23799b33644295d2c6f37c85577142",
+      "clips": 3401,
+      "segments": 4524,
+      "split": "train"
+    },
+    {
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+      "bytes": 2151536640,
+      "sha256": "111c971452e2d35ad2f07505638c9b170f88dc3532679dae710bcea7dcd4b52b",
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+      "segments": 4593,
+      "split": "train"
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+    {
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+      "sha256": "331e499fc3db2bb3f90f8b6be45b131c62ae9ebce8be85864c48e8674aaf4206",
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+      "segments": 4561,
+      "split": "train"
+    },
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+      "sha256": "a0b488fa9add2c409f92f6753d5c7cb7e513bf2f1cd902d1172a789124fcaba0",
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+      "segments": 4506,
+      "split": "train"
+    },
+    {
+      "path": "internet/val.tar",
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+      "sha256": "eb59a303b439fb2777882a7bc87edfa994a1ccaed888380aa9407333b2fddb65",
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+      "segments": 65,
+      "split": "val"
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+      "clips": 14,
+      "segments": 17,
+      "split": "train"
+    }
+  ],
+  "smoke_file": "internet/train-smoke.tar"
+}

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internet/train-0001.tar

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internet/train-smoke.tar

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internet/val.tar

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