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[AAAI2025] Video Repurposing from User Generated Content: A Large-scale Dataset and Benchmark

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<h2 align="center">Video Repurposing from User Generated Content: A Large-scale Dataset and Benchmark</h2>

## News

* :fire: [2024.12.10] Our paper is accepted by AAAI-2025 !

## Introduction

This repository provides the PyTorch implementation for the paper [**Video Repurposing from User Generated Content: A Large-scale Dataset and Benchmark**](https://arxiv.org/abs/2412.08879). The research introduces **Repurpose-10K**, a large-scale dataset designed to tackle the challenge of **long-to-short video repurposing**. The dataset contains over **10,000 videos** and **120,000+ annotated clips**, making it a benchmark for automatic video repurposing.

### What is Video Repurposing?
With the rise of **short-form video platforms** like TikTok, Instagram Reels, and YouTube Shorts, there is a growing need to **efficiently extract engaging segments** from long-form content such as vlogs, interviews, and live streams. Video repurposing involves:
- Identifying **highly engaging** segments from long videos.
- Ensuring **narrative coherence** in the repurposed clips.
- Optimizing for **direct publishing** on social media.

Short-form videos typically vary in length from 0 seconds to 3 minutes, with most content being around 60 seconds, depending on the platform’s specific guidelines and audience preferences. This time frame ensures that the video remains concise, engaging, and optimized for rapid consumption.
<img width="958" alt="Video repurposing task overview" src="https://github.com/user-attachments/assets/5985db0b-7a3c-4064-8dff-ed1ef019ccac" />

### About **Repurpose-10K**
To address the lack of large-scale benchmarks for this task, **Repurpose-10K** was created by collecting **real-world user interactions** on **User Generated Content (UGC)**. The annotation process involves:
1. **Initial segmentation** using AI-assisted tools.
2. **User preference voting** to mark preferred clips.
3. **Manual refinement** of timestamps by content creators.

This ensures high-quality, **human-curated** ground truth labels for training video repurposing models.

## Getting Started

### Setting Up Your Environment
To ensure a smooth experience running the scripts, set up a dedicated `conda` environment by executing the following commands in your terminal:

```bash
conda create -n repurpose python=3.9
conda activate repurpose
pip install -r requirements.txt
```

### Preparing Your Data

The train/validation/test splits are provided in the `/data` directory. Follow these steps for data preparation:

1. Download the source videos using [yt-dlp](https://github.com/yt-dlp/yt-dlp).
2. Extract the required features as mentioned in our paper using these repositories:
   - [video_features](https://github.com/v-iashin/video_features)
   - [panns_inference](https://github.com/qiuqiangkong/panns_inference)
   - [sentence-transformers](https://github.com/UKPLab/sentence-transformers)
   - [whisperX](https://github.com/m-bain/whisperX)

### Training Your Model
To begin training the model, use the command below:

```bash
python main.py --config_path configs/Repurpose.yaml
```

For model evaluation, execute the following command:

```bash
python inference.py --config_path configs/Repurpose.yaml --resume your_ckpt_path
```
Replace `your_ckpt_path` with the actual path to your checkpoint file.

## Citation

```bibtex
@inproceedings{wu2025video,
  title={Video Repurposing from User Generated Content: A Large-scale Dataset and Benchmark},
  author={Wu, Yongliang and Zhu, Wenbo and Cao, Jiawang and Lu, Yi and Li, Bozheng and Chi, Weiheng and Qiu, Zihan and Su, Lirian and Zheng, Haolin and Wu, Jay and others},
  booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},
  volume={39},
  number={8},
  pages={8487--8495},
  year={2025}
}
```

## Acknowledgments

We would like to extend our gratitude to the authors and contributors of the following repositories, which have been instrumental in the development of our implementation:

- https://github.com/ttgeng233/UnAV
- https://github.com/DocF/Soft-NMS

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