AI Video Editing

AI video editing is the use of machine learning to automate or assist the work of cutting, arranging, and modifying recorded footage. Models transcribe dialog, find shot boundaries, follow subjects through a scene, separate them from the background, and generate replacement pixels, so jobs that once took hours of frame-by-frame work, such as captioning an interview or reframing a landscape video for a phone screen, now take minutes. The term covers both assistive features inside traditional editing software and fully automatic tools that turn raw recordings into finished clips.

What AI Handles in an Edit#

Editing software such as Adobe Premiere Pro, DaVinci Resolve, and the transcript-first editor Descript builds many of these features in:

• Speech-to-text editing: A transcript aligned to the timeline lets an editor delete a sentence from the text and remove the matching footage.
• Captions: The same transcript becomes a subtitle draft, which editors review for accurate wording, speaker identification, and timing.
• Scene detection: Models find the cuts in a long recording or a previously edited file and split it into individual shots.
• Auto reframing: Tracking keeps the main subject inside a vertical or square crop as it moves, turning one widescreen master into versions for social platforms.
• Masking and object removal: Segmentation isolates a person or object so it can be color graded separately or removed, and inpainting fills the gap consistently from frame to frame.
• Highlights from action recognition: Models spot goals, dunks, and other key plays, which sports broadcasters use to assemble highlight reels within minutes of the action.
• Slow motion with frame interpolation: Models synthesize in-between frames so footage shot at normal speed can be slowed without stutter.
• Audio cleanup: Speech enhancement models strip background noise and room echo from dialog recorded in imperfect conditions.
• Redaction for data privacy: Faces, people, or license plates are detected and blurred before footage is shared.

The Vision Models Behind the Features#

Most visual editing features chain three computer vision tasks. Object detection finds each subject in a frame and draws a bounding box around it. Object tracking links those detections across frames with stable IDs, which is what lets a reframe or blur stay attached to the same person as they walk through a shot. When an effect needs the subject's exact outline, instance segmentation supplies a pixel-level mask for each object. All three are video understanding models, which describe footage without changing it, and an editing feature runs them first to decide what to change.

Generative models cover the rest. Inpainting models remove unwanted objects, and video generation models restyle a shot, extend it by a few seconds, or create footage that never existed. Automated edits break in predictable places: fast motion and motion blur, subjects crossing in front of each other, scene cuts where a track should end, and small or partly visible subjects. Because the same techniques can alter what a recording appears to show, they also produce deepfakes, and publishers can attach Content Credentials that record which tools edited a file.

Automating Redaction#

Redaction is one of the simplest edits to automate for teams publishing event, street, or workplace footage. The Ultralytics object blurring solution runs a YOLO26 detector with tracking on every frame and blurs each box of the chosen classes; class 0 in the COCO-pretrained model is "person":

import cv2
from ultralytics import solutions

cap = cv2.VideoCapture("https://github.com/ultralytics/assets/releases/download/v0.0.0/decelera_portrait_min.mov")
w = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
h = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
fps = cap.get(cv2.CAP_PROP_FPS)
writer = cv2.VideoWriter("blurred.mp4", cv2.VideoWriter_fourcc(*"mp4v"), fps, (w, h))

# Detect people (COCO class 0) with YOLO26 and blur every box in every frame
blurrer = solutions.ObjectBlurrer(model="yolo26n.pt", classes=[0])
while cap.isOpened():
    success, frame = cap.read()
    if not success:
        break
    results = blurrer(frame)
    writer.write(results.plot_im)

cap.release()
writer.release()

The output, blurred.mp4, shows every detected person blurred and outlined with a labeled box for review, and blur_ratio (default 0.5) sets the blur strength. OpenCV writes video frames only, so the original audio has to be added back in an editor or with FFmpeg. People cut off at the frame edge or seen at unusual angles are the detections most likely to be missed, and each frame with a missed detection leaves that person unblurred in the output.