Metadata-only reconstruction release: sources + manifest + reconstruct.py (videos not redistributed)
71d21b1 verified | license: cc-by-4.0 | |
| pretty_name: DENSEWORLD-115k | |
| language: | |
| - en | |
| task_categories: | |
| - video-classification | |
| - other | |
| tags: | |
| - video | |
| - world-models | |
| - jepa | |
| - urban | |
| - global-south | |
| - india | |
| - youtube | |
| - metadata-only | |
| size_categories: | |
| - 100K<n<1M | |
| # DENSEWORLD-115k | |
| **A large-scale video benchmark of populous, crowded, and chaotic Global South urban environments**, used to study world models (JEPA) under soft spatial boundaries, extreme agent heterogeneity, persistent occlusion, and rapid social negotiation. | |
| - **115,687** clips (4–10 s each) | |
| - **714** long-form source videos across **22 Indian cities** | |
| - Drive-through, walk-through, and aerial (drone) viewpoints; markets, ghats, junctions, flyovers, beaches, and more | |
| ## ⚠️ This is a metadata-only dataset (no videos included) | |
| The source clips are derived from **public YouTube videos** and remain under their creators' copyright. We therefore **do not redistribute any video files**. Instead, this repository ships the **source video list, a clip manifest, and a deterministic reconstruction script** so you can rebuild the exact clips locally from YouTube. This mirrors the standard practice of YouTube-derived datasets (e.g. Panda-70M, HD-VILA-100M, HowTo100M). | |
| ## Repository contents | |
| ``` | |
| denseworld-115k/ | |
| ├── README.md # this card | |
| ├── requirements.txt # yt-dlp, scenedetect[opencv] | |
| ├── reconstruct.py # download → scene-detect → split → rebuild the 115,687 clips | |
| ├── sources.json # 714 YouTube source videos (id, url, section, n_clips, title) | |
| ├── clips.csv # 115,687-clip manifest (clip_key, section, base_video_id, chunk, duration…) | |
| └── data/ | |
| └── data_prep/ | |
| ├── clip_durations.json # per-video clip manifest (ground truth for verification) | |
| ├── city_matrix.json # per-city × capture-type coverage counts | |
| └── word_frequency.json # source-title word frequencies + scene taxonomy mapping | |
| ``` | |
| ## Reconstructing the clips | |
| ```bash | |
| pip install -r requirements.txt # yt-dlp + PySceneDetect | |
| # install ffmpeg + ffprobe (brew install ffmpeg / apt-get install ffmpeg) | |
| python reconstruct.py --out ./denseworld_clips --limit 1 # smoke test (1 video) | |
| python reconstruct.py --out ./denseworld_clips # full rebuild (all 714 videos) | |
| python reconstruct.py --out ./denseworld_clips --verify-only # check counts vs the manifest | |
| ``` | |
| The pipeline is **deterministic** and reproduces the released clips exactly: | |
| 1. **Download** each source video at 480p (`yt-dlp`). | |
| 2. **Detect scene boundaries** (PySceneDetect `ContentDetector`, threshold 15.0). | |
| 3. **Greedy-split** into contiguous 4–10 s clips at scene boundaries. | |
| 4. **Encode** each clip (`ffmpeg` libx264 CRF 28, AAC 128k). | |
| Clips are written to `<out>/<section>/<video_id>-<NNN>.mp4` (e.g. `goa/walking/04YKvC8kAgI-000.mp4`), matching the `clip_key`s in `clips.csv`. | |
| > Some source videos may become unavailable over time (deleted or made private on YouTube); `reconstruct.py` skips these and reports the shortfall in its verification summary. Three very long videos were originally cut in fixed windows rather than whole-video scene detection, so their clip boundaries may differ slightly (<1% of the dataset). | |
| ## Data fields | |
| **`sources.json`** → `videos: [ { … } ]` | |
| | field | description | | |
| |---|---| | |
| | `id` | 11-char YouTube video ID (source URL = `https://www.youtube.com/watch?v=<id>`) | | |
| | `url` | full YouTube watch URL | | |
| | `sections` | list of `city/capture-type` sections this video contributes to (e.g. `goa/walking`) | | |
| | `n_clips` | number of clips produced from this video | | |
| | `n_chunks` | >0 only for the 3 pre-chunked long videos | | |
| | `title`, `category` | source title and collection category (`drive_tours`, `walking_tours`, `drone_views`, `tier2_cities`) | | |
| **`clips.csv`** (115,687 rows) | |
| | column | description | | |
| |---|---| | |
| | `clip_key` | `section/video_id/file` — canonical clip identifier | | |
| | `section` | `city/capture-type` (e.g. `mumbai/drive`) | | |
| | `base_video_id` | the 11-char YouTube ID | | |
| | `chunk` | fixed-window chunk index for the 3 long videos, else empty | | |
| | `video_id` | manifest key (`base_video_id` or `base_video_id-<chunk>`) | | |
| | `clip_index` | clip order within its video/chunk | | |
| | `duration_sec`, `size_mb`, `status` | encoded clip stats | | |
| ## License & responsible use | |
| - The **metadata and code** in this repository are released under **CC-BY-4.0**. | |
| - The **videos are NOT included** and are **not** covered by this license — they remain the property of their original YouTube uploaders and are subject to YouTube's Terms of Service. Use the reconstructed clips for research purposes and in accordance with those terms. | |
| - **Takedown:** if you are a rights holder and want a source removed from `sources.json`, please open an issue on this repository and we will remove it. | |
| ## Citation | |
| ```bibtex | |
| @article{wanaskar2026factorjepa, | |
| title = {FactorJEPA: Factorizing Monolithic Futures into Layout--Agent--Interaction | |
| Channels for Crowded and Chaotic Global South Urban Worlds}, | |
| author = {Wanaskar, Kapil and Jena, Gaytri and Chadha, Aman and Jain, Vinija | |
| and Sharma, Vasu and Das, Amitava}, | |
| year = {2026}, | |
| note = {Preprint. Update with arXiv ID when available.} | |
| } | |
| ``` | |