Video datasets for AI training. Captioned over time, licensed per clip.
Video datasets for AI training built from footage the filmmakers agreed to license, and commissioned when the motion you need has never been filmed. Timestamped captions, action segments, camera metadata, and releases for everyone on screen, with frame hashes on every clip and a signed receipt on every delivery.
What is a video dataset for AI training?
A video dataset for AI training is a set of clips paired with labels that describe what happens in them over time: timestamped captions, when each action and event begins and ends, how the camera moved, where the shots cut, where objects went, and the technical facts about each clip. A video model has to learn motion, sequence, and cause and effect, and a label that describes one frame teaches none of those.
That makes video the data type where annotation quality and rights both bite hardest. A clip-level caption wastes most of the clip. A trimmed copy of a stock clip passes a file hash and lands in your set twice. And nearly every piece of footage has a person in it, which means a release question on nearly every clip.
eeko's data practice licenses footage from filmmakers and videographers who opted in through our creator licensing program, commissions capture through the data capture process when a distribution is missing, and has every clip captioned over time by writers working to your style guide. Clips are frame-hashed, origin-checked, released, and listed on a signed receipt, the standard we hold our own production AI systems to.
Video captioning datasets that describe what changes.
The useful label on a clip is not what it shows. It is what happens, in what order, and what the camera did meanwhile.
What a clip carries when it leaves us
- Temporal captions — timestamped descriptions of actions, state changes, and camera behavior, at the granularity your style guide sets.
- Clip-level summary — one description of the whole clip, for retrieval and filtering.
- Action and event segments — start and end times against your taxonomy, with reviewer agreement reported per batch.
- Camera and shot metadata — pan, tilt, dolly, handheld, static, and shot boundaries, tagged per clip.
- Object tracks — where the spec asks, boxes tracked across frames for the classes you name.
- Label source — for every label, whether a person wrote it, corrected an AI draft, or reviewed another person's work.
Domain footage is handled by people who know it: surgical video by clinicians, industrial processes by engineers, manipulation tasks by teleoperation specialists.
Licensed video datasets, six ways to fill a spec.
Subjects, motions, settings, camera behavior, resolution, caption density, and release requirements are written down first. Then the footage comes from wherever the spec is best served.
Consented video footage, released clip by clip.
Almost every clip has a person in it. The release is the difference between footage you can train on and footage you can only look at.
How people on screen are handled
- Consent before filming — participants and subjects read what is recorded, what it may train, and what they are paid, then accept in writing.
- Release bound to the clip — not to a project folder. If a clip is delivered, its release reference is on the record for that clip.
- No release, no delivery — footage with an identifiable person and no consent on file is held back.
- Stripped metadata — location and personal fields are removed before anything ships.
- Catalogue footage — creators confirm releases for the people in their clips when they list; we spot-check.
- Paid at a stated rate — participants and filmmakers see the rate before they record, and accepted work pays automatically.
Every clip frame-hashed, every delivery signed.
Video duplicates hide behind a trim and a transcode. Synthetic clips hide behind a filename. Both are checked at intake.
Video datasets for AI training, by industry.
From a shot list to a sealed dataset.
Fixed scope, fixed price per clip or per hour of footage, set after the pilot. A real person replies inside one business day.
Video dataset questions.
What is a video dataset for AI training?
A video dataset for AI training is a collection of clips paired with labels that describe what happens in them over time: captions with timestamps, the beginning and end of each action and event, camera motion tags, shot cuts, and object tracks, plus technical facts: duration, resolution, codec, and frame rate. Unlike an image set, the label has to capture change and cause, not a single frame. A usable set also carries a licence and releases for every clip.
How much does video training data cost?
Quotes are per clip or per hour of footage, issued once scoping is complete. The drivers are the catalogue-to-commissioned split, resolution and duration, which releases are required, caption density, whether you need segments or object tracks, specialist involvement for domain footage, and exclusivity. Commissioned footage with dense captions and people on screen is the expensive end; catalogue clips with one caption each are the cheap end. A pilot batch fixes the production rate.
Can you film footage that does not exist in any catalogue?
Yes. Commissioned capture is how we fill a distribution gap: a specific action performed many ways, an environment such as a warehouse or a kitchen, a camera move, a first-person perspective, or domain footage such as an industrial process. We write a shot list with you, brief filmmakers or participants from the eeko network, collect releases on set, and run the footage through the same intake and review as catalogue clips. Commissioned work can be licensed exclusively.
What does a temporal caption look like?
A temporal caption is a sequence of timestamped descriptions across a clip rather than one sentence for the whole thing: at 0:00 a hand reaches for the kettle, at 0:03 it lifts and tilts, at 0:06 steam rises as water pours. Granularity, timestamp precision, and vocabulary are set in a shared style guide. Writers describe actions, state changes, and camera behavior, a second reviewer verifies timing and accuracy on an agreed share, and every caption records its author.
How do you catch trimmed or re-encoded duplicate clips?
A file hash alone misses a clip that has been trimmed, transcoded, or resized, so every clip also gets hashes computed per frame at intake. Those frame hashes are compared against everything already registered, which flags a copy of registered footage even when the file bytes are completely different. When two submissions appear to share footage, both are held until the ownership question is answered, so disputed clips never reach your delivery.
Is it legal to train a video model on footage with people in it?
It is when the people on screen agreed to it. A clip showing an identifiable person is offered for training only with that person's consent recorded, and the release is bound to the clip so it travels with the delivery. Participants in commissioned and egocentric sessions agree in advance to what is filmed and how it may be used. Location metadata is stripped before delivery. Footage without the right releases is held back rather than shipped.
Explore related data.
Tell us what your model needs to watch.
Subjects, motions, settings, hours, and the job the model has to do. A data lead replies inside one business day with a sourcing plan and a pilot proposal. No deck. A ranked list of what to film first.
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