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README.md

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+---
+license: cc-by-4.0
+language:
+- en
+task_categories:
+- image-to-text
+- image-feature-extraction
+tags:
+- video
+- world-model
+- frame-sequence
+- temporal
+- openvid
+size_categories:
+- 100K<n<1M
+configs:
+- config_name: index
+  data_files: sequences.jsonl
+---
+
+# OpenVid Frame Sequences — 1M adjacent frame pairs
+
+Short, single-shot frame sequences cut from [OpenVid-1M](https://huggingface.co/datasets/nkp37/OpenVid-1M),
+built to train and evaluate models on **what changes between two frames half a second apart**.
+
+One sample = **10 consecutive frames, 0.5 s apart** (a 4.5 s span) → **9 adjacent frame pairs**.
+
+```
+[f00] --0.5s--> [f01] --0.5s--> [f02] ... [f09]
+      ^ the thing you describe / predict
+```
+
+| | |
+|---|---|
+| Sequences | **116,596** |
+| Frames per sequence | 10 (0.5 s apart, t = 0.0 … 4.5 s) |
+| **Adjacent frame pairs** | **1,049,364** |
+| Source clips | 87,002 (**at most 2 sequences per clip**) |
+| Images | 1,165,960 JPEGs, 768 px wide, q=3, mean 44 KB |
+| Download size | ~53 GB (frames) + 145 MB (index) |
+
+Source videos are **not** included — see [OpenVid-1M](https://huggingface.co/datasets/nkp37/OpenVid-1M)
+for those. Every sequence carries the clip name and timestamps, so the source frames are recoverable.
+
+## Why this exists
+
+Video-caption datasets describe a whole clip. Training a model to reason about *change*
+needs pairs that are close enough in time to share a scene, but far enough apart that
+something actually happened. This dataset fixes that interval at 0.5 s and filters for
+clips where motion is real, so that a difference caption between `f_i` and `f_i+1` is
+never "nothing changed".
+
+Measured on a uniform random sample: **mean absolute pixel change between adjacent frames
+is 16.6 / 255** (median 15.5), and only **1.0%** of sequences are effectively static.
+
+## Selection criteria
+
+Applied to the full OpenVid-1M metadata (1,019,957 clips) before downloading a single byte:
+
+```
+motion score      ∈ [5.27, 60]     # 5.27 = top 29.5% of the candidate pool
+aesthetic score   ≥ 5.0
+duration          ≥ 4.75 s (one window) / ≥ 9.75 s (two windows)
+sequences/clip    ≤ 2               # so no single video dominates the data
+caption           must not match  talk|talking|interview|podcast
+clip name         must not start with  celebv_  or  pixabay_
+camera motion     unconstrained
+```
+
+Capping at 2 sequences per clip matters: without it a handful of long videos contribute
+hundreds of near-identical sequences each.
+
+Camera motion is deliberately **not** constrained — pans and zooms are themselves valid
+visual change. The resulting mix is **63.4% static / 36.6% moving**
+(pan_right 7.8%, pan_left 7.0%, undetermined 6.5%, zoom_in 2.8%, tilt_up 2.2%, …).
+
+## Composition
+
+| | |
+|---|---|
+| camera | static 63.4% · moving 36.6% |
+| source resolution | 64.9% from 1080p originals (all resized to 768 px wide) |
+| motion score | min 5.27 · p25 6.76 · **median 9.74** · p75 20.95 · max 60.00 |
+| source clip length | median 9.8 s |
+| windows per clip | 1 window: 57,408 clips · 2 windows: 29,594 clips |
+
+## Files
+
+```
+frames/frames-000.tar … frames-063.tar   64 shards, ~850 MB each, 53 GB total
+sequences.jsonl                          116,596 lines — the index
+meta/manifest.csv                        111,536 rows: the full filtered candidate list
+meta/part_index.jsonl                    1,019,957 rows: clip -> (OpenVid zip part, member, bytes)
+scripts/                                 the pipeline that produced this
+docs/                                    build runbook and result notes (Korean)
+```
+
+Each tar extracts to `frames/<2-hex shard>/<clip>__w<N>/f00.jpg … f09.jpg`, which is exactly
+the path written in `sequences.jsonl`. Extract every shard into the same directory and the
+index paths resolve as-is.
+
+The 2-hex sharding (`md5(sequence_id)[:2]`) keeps any one directory under ~500 entries,
+which matters on network filesystems.
+
+## Getting it
+
+```bash
+pip install huggingface_hub
+hf download junha1125/openvid-frame-sequences-1M --repo-type dataset --local-dir openvid_seq
+cd openvid_seq && for t in frames/*.tar; do tar -xf "$t"; done
+```
+
+## A line of `sequences.jsonl`
+
+```json
+{
+  "id": "<clip>__w0",
+  "video": "<clip>.mp4",
+  "frames": ["frames/90/<clip>__w0/f00.jpg", "...", "frames/90/<clip>__w0/f09.jpg"],
+  "timestamps": [0.0, 0.5, 1.0, 1.5, 2.0, 2.5, 3.0, 3.5, 4.0, 4.5],
+  "step_sec": 0.5,
+  "n_frames": 10,
+  "video_caption": "the original OpenVid caption for the whole clip",
+  "meta": {"seconds": "...", "fps": "...", "motion": "...",
+           "aesthetic": "...", "camera": "...", "hd": "1"}
+}
+```
+
+Window `w` frame `i` is the source video at `w * 5.0 + i * 0.5` seconds.
+`video_caption` is the OpenVid caption for the *entire* clip, not for this window —
+it is useful as context for a captioning prompt, not as a label.
+
+```python
+import json
+seqs = [json.loads(l) for l in open("sequences.jsonl")]
+pairs = [(s["frames"][i], s["frames"][i+1], s) for s in seqs for i in range(9)]
+len(pairs)   # 1,049,364
+```
+
+## Integrity
+
+Every one of the 1,165,960 frame paths in `sequences.jsonl` was stat-checked against disk:
+0 missing, 0 orphan JPEGs, 0 duplicate ids, 0 clips over the 2-sequence cap, all sequences
+exactly 10 frames at exactly 0.5 s spacing, no sequence below the motion floor.
+The 64 tars hold 1,282,812 entries = 1,165,960 JPEGs + 116,596 sequence dirs + 256 shard dirs.
+
+## Known limitations
+
+- **Shot changes: ~3.2% of sequences.** OpenVid's scene segmentation is not perfect, so a
+  small fraction of sequences contain a cut. If a pair looks like two unrelated scenes,
+  drop it. This is measurable — a single adjacent pair whose pixel delta is several times
+  the sequence's own median is almost always a cut.
+- **Residual talking-head footage.** The caption filter only matches
+  `talk|talking|interview|podcast`, so clips where someone talks to the camera without those
+  words in the caption (driving-and-talking, lecture footage) survive. Tighten
+  `TALKING` in `scripts/select_clips.py` if that hurts your use case.
+- `video_caption` describes the whole source clip, so it can mention things not visible in
+  a given 4.5 s window.
+- Frames are re-encoded JPEG at q=3 from already-compressed video; fine detail is lossy.
+
+## Reproducing / extending
+
+`scripts/` contains the full pipeline. `meta/manifest.csv` is fixed-order with a `cum_gb`
+column, so raising the byte budget downloads only the next slice:
+
+```bash
+python3 scripts/fetch_clips.py --budget-gb 800 --workers 32   # this release used 600
+python3 scripts/extract_frames.py --workers 176               # existing windows are skipped
+python3 scripts/verify_dataset.py --full
+```
+
+| budget | clips | sequences | adjacent pairs |
+|---|---|---|---|
+| **600 GB** (this release) | 84,362 | 111,417 | 1,002,753 |
+| 800 GB | 96,176 | 128,540 | 1,156,860 |
+| 1,000 GB | 104,216 | 140,898 | 1,268,082 |
+| 1,390 GB (all) | 111,536 | 153,391 | 1,380,519 |
+
+This release has slightly more than the 600 GB row because 2,640 clips from beyond the
+budget were already on disk and were extracted too.
+
+Note for anyone pulling clips out of the OpenVid zips: `zipfile.ZipFile.read()` costs 3–4
+HTTP round trips per member and Hugging Face throttles on request count, not bytes.
+`scripts/hfzip.py:read_member()` fetches the local header and payload in one Range request
+and parses it directly — 2.5× faster in practice (19 → 58 MB/s).
+
+## License and attribution
+
+**CC-BY-4.0**, inherited from OpenVid-1M. These are frames extracted from OpenVid-1M clips;
+all credit for the underlying video collection and its captions goes to its authors.
+
+```bibtex
+@article{nan2024openvid,
+  title   = {OpenVid-1M: A Large-Scale High-Quality Dataset for Text-to-video Generation},
+  author  = {Nan, Kepan and Xie, Rui and Zhou, Penghao and Fan, Tiehan and
+             Yang, Zhenheng and Chen, Zhijie and Li, Xiang and Yang, Jian and Tai, Ying},
+  journal = {arXiv preprint arXiv:2407.02371},
+  year    = {2024}
+}
+```
+
+Intended for research use. The underlying clips are sourced from public video platforms;
+the OpenVid authors' terms and takedown process apply to them.