Content type guide
Video data for AI training: what can be licensed
Video can teach a model appearance, motion, activity, environments, and the relationship between actions and outcomes. The most useful type depends on whether the task is generation, perception, robotics, or a multimodal model.
A finished video file usually contains several rights layers: the recording, people in frame, locations, music, logos, embedded works, and sometimes a platform’s distribution terms. A licensable training corpus is built by understanding those layers before a buyer asks for them.
This is a practical map of the categories that organisations and creators may be able to prepare for AI use. It is not a claim that every category is already represented in FIUND inventory; availability should follow a collection-specific rights and quality review.
Types of video content to explore
Film & professional video
Original masters, production footage, and archives where chain of title, talent, location, and music rights determine the usable slice.
Creator & UGC video
Real-world, creator-owned footage that can be valuable when source files, permissions, and people in frame are documented.
Egocentric video
Wearable-camera footage for robotics, assistants, and world models, often paired with IMU, gaze, or task annotations.
Motion capture
Performance movement rather than pixels: rig-ready skeletal data for avatars, pose, gestures, and humanoid systems.
How to prepare a collection
- Start from original or highest-quality camera files, and preserve resolution, frame rate, camera metadata, and any separate audio tracks.
- Make a rights map that distinguishes the filmer or production owner, on-camera people, locations, music, trademarks, and embedded third-party media.
- Flag rather than hide limitations: known unlicensed music, bystanders, logos, restricted locations, edits, and duplicate uploads.
- Describe the footage by usable attributes such as scene, activity, viewpoint, capture device, duration, lighting, and spoken-language presence.
- For new capture, decide the target task and consent process first; the camera setup, annotations, and release language flow from that brief.
Keep learning
How to make money creating content for AI training
How archive licensing and commissioned collection differ for creators and production teams.
LiDAR & 3D point clouds
A companion guide for 3D geometry, sensor calibration, privacy, and site permissions.
How to license LiDAR data for AI
A step-by-step readiness guide for point clouds, site permissions, privacy, calibration, and labels.
What to check before licensing a dataset
A buyer checklist for rights, samples, metadata, and delivery terms.
Frequently asked questions
Can a production company license footage it made years ago for AI training?
Possibly, but owning the footage is only the start. Review talent agreements, location permissions, music and stock elements, client terms, and what the intended AI licence covers. A clean subset may be ready before an entire archive is.
Does a creator own enough rights to license a video they posted?
A creator may own the recording, but identifiable people, music, locations, and platform terms can create additional issues. The source file and a clear list of everyone with rights or consent are more useful than a download from a public platform.
What video metadata should be prepared first?
At minimum: source file, capture date, resolution, frame rate, device or camera, location category, scene or activity, people and consent status, audio/language status, and a rights or restrictions note. Add labels only after the core record is trustworthy.
Explore other content types
Planning a video data program?
Use the guide to define the content, rights, and documentation that a real collection will need before you decide what to build or license.
Discuss a future data program