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uva-cv-lab/OmniShotCut_paper
OmniShotCut_paper is a machine learning model from uva-cv-lab. Use it for the machine learning task on the model card, and read the license before you ship it in a product. The card lists the license as mit.
Authors: Boyang Wang, Guangyi Xu, Jiahui Zhang, Zhipeng Tang, Zezhou Cheng Affiliation: UVA Computer Vision Lab
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From the Hugging Face model README
Authors: Boyang Wang, Guangyi Xu, Jiahui Zhang, Zhipeng Tang, Zezhou Cheng
Affiliation: UVA Computer Vision Lab
OmniShotCut is a sensitive and informative shot boundary detector. It detects shot changes across diverse sources (anime, vlog, game, shorts, sports, screen recording, etc.) and recognizes Sudden Jumps and Transitions (dissolve, fade, wipe, ...) with a Shot-Query-based Video Transformer.
| Model | Description | Link |
|---|---|---|
| OmniShotCut v1.5 (recommended) | Hidden dim 576, 52.8M params. Best real-world quality: fewer false cuts on continuous camera motion / lighting changes, and better dense fast-cut recall. | Download |
| OmniShotCut (paper) | 3 encoders, 34.5M params. Reproduces the paper (Table 1) numbers. | Download |
| OmniShotCut (preview) | 6 encoders, 41M params. Early preview release. | Download |
Install:
pip install git+https://github.com/UVA-Computer-Vision-Lab/OmniShotCut.git
Run shot boundary detection:
import omnishotcut
cut_model = omnishotcut.load("uva-cv-lab/OmniShotCut_v1.5", filename="OmniShotCut_ckpt.pth")
ranges = cut_model.inference("video.mp4", mode="clean_shot")
@article{wang2026omnishotcut,
title={OmniShotCut: Holistic Relational Shot Boundary Detection with Shot-Query Transformer},
author={Wang, Boyang and Xu, Guangyi and Zhang, Jiahui and Tang, Zhipeng and Cheng, Zezhou},
journal={arXiv preprint arXiv:2604.24762},
year={2026}
}