AI video clipping
AI video clipping is the automatic creation of short clips from a longer video or a live stream, using AI to find the moments worth sharing. Models read signals such as the transcript, changes in audio energy, scene and shot changes, faces and on-screen action, then score candidate segments and pick start and end points that land on clean sentence and shot boundaries. Many clipping systems then render each clip ready to post: reframed for vertical screens, captioned for viewing on mute and given a title.
How does AI video clipping work?
The system first builds a map of the video. Speech-to-text gives the words and when they were said. Audio analysis finds peaks such as crowd noise, applause or raised voices. Shot boundary detection finds the cuts, and scene detection groups shots into scenes. Vision models track faces and on-screen action.
Candidate segments are scored on those signals: a complete thought in the transcript, a spike in crowd noise, a change of speaker, a named topic. The top candidates get boundaries snapped to the start of a sentence and a shot change, so a clip does not begin mid-word.
Rendering is where video engineering matters. Cutting without re-encoding can only start on a keyframe, which may be seconds away from the chosen point. Frame-accurate clips are re-encoded from the source, which is also when reframing, burned-in captions and a title card are added. The finished clip is then packaged for streaming or exported as an MP4.
How does clipping a live stream work?
A live stream is recorded as it is delivered, as a growing list of segments a few seconds long. That recording is what makes live clipping possible. A clip from an active stream is cut from segments that have already arrived, behind the live edge, while the stream keeps running.
The clip can be made by time range, when an operator marks the moment, or by an AI agent that watches the transcript and audio for signals such as a commentator's reaction or a crowd surge. Either way, the clip only needs the segments around the moment, so it can be published while the event continues.
That timing is the reason live sports and news teams clip during the event. A goal posted a few minutes after it happens reaches fans while they are still talking about it; the same clip posted after the final whistle competes with everyone else's. The full stream is still archived for on-demand replay.
AI video clipping vs. manual clipping
Manual clipping works when someone already knows the moment. An editor marks in and out points, or an operator hits a button during a live event, and the clip is cut by time range through an API or an editing tool. It is precise and fully controlled.
AI video clipping is for when nobody has time to watch everything. Hours of lectures, podcasts or long streams can hold dozens of usable moments that no editor will find by scrubbing. The agent proposes candidates with scores, and a person approves, trims or discards them.
The two are often combined. A news desk might clip a known quote by time range within seconds, while the clipping agent works through the rest of a two-hour briefing for other moments. Review stays important: an AI-picked clip can be technically clean but lose meaning without its context, which is a problem for news and anything with legal weight.
How does AI video clipping reframe for vertical video?
Most source video is 16:9, wide. Vertical feeds want 9:16, tall. A centre crop throws away most of the frame and often cuts off the person speaking, so reframing systems track the subject instead. Face and body detection find the speaker or the action, and a virtual camera follows them inside the wide frame, with smoothing so the crop does not jitter.
Some shots resist cropping. Two people talking at opposite edges, a wide shot of a pitch or a slide with text across the whole frame cannot fit in a narrow window. Systems handle those with split screens, a scaled frame over a blurred background, or by switching the crop target when the speaker changes.
Captions add a constraint. Burned-in captions need a safe zone that avoids the platform's own buttons and text overlays. Once the clip is rendered vertically, it can also be offered in square and other aspect ratios from the same source.
How does FastPix handle AI video clipping?
FastPix creates clips from on-demand videos and from active live streams through the API. If you know the moment, you cut by time range. If you do not, the AI clipping agent segments the video, scores the candidate moments and renders clips with captions and a title. In-video AI can reframe the output to every aspect ratio, so a 16:9 source becomes 9:16 for vertical feeds, and the AI video editor can overlay text and images on each clip. A cover image for each clip is a FastPix thumbnail taken at any time in the clip.
On live streams, you can clip a highlight mid-event from the recorded part of the stream while the broadcast continues. The whole event is kept too: auto_archive publishes it as an on-demand replay on the same playback ID once it ends. Live ingest takes RTMP, RTMPS or SRT, and delivery is LL-HLS at 4 to 7 seconds glass to glass, so clips come from the same pipeline that streams the event.
Clips play in the open-source FastPix Player, with native SDKs for web, Android, iOS and Flutter and 9:16 full-bleed playback with swipe-to-next, or in any HLS player. Your app decides which clips publish straight away and which go to review.
Clipping work is billed per minute processed, on the same bill as encoding and streaming, with no token billing. Clips played in your own player are billed per minute watched, and stored clips per rendition-minute. Standard encoding is free up to 4K. The free plan's 30 live minutes, no card, cover a practice stream.
