| --- |
| license: cc-by-4.0 |
| task_categories: |
| - video-classification |
| size_categories: |
| - 100K<n<1M |
| tags: |
| - video |
| - video-compression |
| source_datasets: |
| - nkp37/OpenVid-1M |
| --- |
| |
| # OpenVid GOP cache for HFTV |
|
|
| Fixed-size one-second video clips, packed for training a spatiotemporal |
| autoencoder. Derived from [OpenVid-1M](https://huggingface.co/datasets/nkp37/OpenVid-1M) |
| (Nan et al., *OpenVid-1M: A Large-Scale High-Quality Dataset for |
| Text-to-video Generation*, ICLR 2025), used and redistributed under |
| **CC BY-4.0**; this derivative carries the same licence. |
|
|
| ## Why it exists |
|
|
| OpenVid-1M ships as 12.4 TB of ~50 GB zip parts, so selecting clips by |
| caption cannot reduce what you *fetch* -- only what you keep. This is |
| that selection done once: the filter below applied, every clip decoded |
| to a fixed geometry, and the result packed into a flat memmap that a |
| training job reads directly. Built from 16 of 186 parts. |
|
|
| ## Contents |
|
|
| | directory | clips | GOPs | filter | |
| |---|---:|---:|---| |
| | `openvid-head/` | 10060 | 29689 | static, low motion, >= 1 s, **and** a talking-head caption | |
| | `openvid-nonperson/` | 38281 | 78593 | static, low motion, >= 1 s, and **no person mentioned at all** | |
|
|
| They are disjoint, and a large middle is **dropped rather than |
| assigned**: clips whose caption mentions a person but does not describe |
| a talking head go into neither split. That is what makes the contrast |
| real. An earlier version of this dataset had only "head" and |
| "everything else", and "everything else" was 86% people -- 61% of it |
| mentioning `wearing`, 26% `hair`, 19% `face` -- so a sweep across the |
| two ran from roughly 86% head to 100% head rather than 0% to 100%. |
|
|
| Talking-head captions match: |
|
|
| ``` |
| speaking |
| interview |
| talking |
| microphone |
| looking at the camera |
| portrait |
| podcast |
| news anchor |
| ``` |
|
|
| ## Format |
|
|
| Each directory holds `gops.u8` and `index.json`. |
|
|
| - `gops.u8` -- raw `uint8`, C-order, shape `(count, 12, 120, 160, 3)`, |
| RGB. One row is one GOP: **12 frames at 12 fps = 1.000 s**. |
| - `index.json` -- `{frames, height, width, fps, count, sources}`, where |
| `sources[i]` is the OpenVid filename GOP `i` came from. Up to |
| 3 consecutive GOPs come from one clip. |
|
|
| ```python |
| import json, numpy as np |
| meta = json.load(open("openvid-head/index.json")) |
| gops = np.memmap("openvid-head/gops.u8", np.uint8, "r", |
| shape=(meta["count"], 12, 120, 160, 3)) |
| ``` |
|
|
| Clips are **centre-cropped to 4:3 and then scaled** -- never stretched. |
| Letterbox bars are detected and removed before the crop. |
|
|
| ## Caveats |
|
|
| - 120x160 at 12 fps is a deliberate reduction, chosen for a |
| narrowband analog video mode. This is not a general-purpose video |
| corpus; nothing here is at OpenVid's original quality. |
| - The filter is metadata-driven. `camera motion` and `motion score` |
| are OpenVid's own annotations and inherit their errors. |
| - Captions are matched by regular expression, so both splits contain |
| what a caption *says*, not what a detector found. The non-person |
| split in particular is "no person was mentioned", which is not the |
| same as "no person is visible" -- expect some contamination, and |
| check the pictures rather than trusting the label. |
|
|
| Built by [`scripts/prepare_openvid_job.py`](https://github.com/arodland/HFTV). |
|
|
