Datasets:

arodland
/

openvid-hftv-gops

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 (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.

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.

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.

OpenVid GOP cache for HFTV

Why it exists

Contents

Format

Caveats