UVA-Computer-Vision-Lab

UVA-Computer-Vision-Lab / OmniShotCut

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OmniShotCut is a sensitive and more informative SoTA on Shot Boundary Detection task.

45
3
100% credibility
Found May 01, 2026 at 45 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
Python
AI Summary

OmniShotCut is an AI tool that detects and classifies shot boundaries and transitions in videos from diverse sources like anime, vlogs, games, and sports.

How It Works

1
🎥 Discover video shot analyzer

You hear about a smart tool that breaks down videos into scenes and spots fancy edits like fades or wipes.

2
🌐 Visit the online demo

Head to the free web demo where you can try it right in your browser without any setup.

3
📤 Upload your video

Pick any video from your phone or computer, like a family trip or game clip, and drop it in.

4
🚀 Hit analyze

Click the button and watch as it magically scans your video for scene changes and smooth transitions.

5
🖼️ View colorful breakdowns

Scroll through image pages showing your video frames with bars highlighting each shot and edit type.

6
📊 Check the shot details table

Read the simple list of every shot's start time, end time, and what kind of change happened.

🎉 Master your video edits

Now you perfectly understand every cut and transition, ready to edit or share insights easily.

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Star Growth

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AI-Generated Review

What is OmniShotCut?

OmniShotCut is a Python tool for shot boundary detection that scans videos and pinpoints cuts, sudden jumps, and transitions like dissolves, fades, or wipes. It handles diverse sources—anime, vlogs, games, shorts, sports, screen recordings—delivering sensitive, informative results beyond basic change detection. Run a Gradio demo locally with `python app.py` or infer via CLI (`python test_code/inference.py --input_video_path video.mp4 --mode clean_shot`), getting visualized shot timelines and JSON outputs.

Why is it gaining traction?

It claims SOTA performance on the shot boundary detection task, standing out by classifying transition types accurately across messy, real-world videos where simpler detectors falter. The Hugging Face Space demo and pre-trained weights let devs test instantly without setup hassles, plus modes like "clean_shot" filter to hard cuts only. Early traction comes from its plug-and-play inference on varied content.

Who should use this?

Video ML engineers automating editing pipelines, content platforms segmenting uploads for analysis, or researchers prototyping video understanding apps. Ideal for devs processing user-generated clips like TikToks or gameplay footage needing quick boundary detection.

Verdict

Try the demo for shot boundary detection prototypes—it's accessible and promising—but with 45 stars and 1.0% credibility score, it's early-stage; await training code and benchmarks for production. Solid start for Python video workflows.

(198 words)

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