---
title: tgve_plus
canonical_url: "https://www.modelscope.cn/datasets/facebook/tgve_plus"
md_url: "https://www.modelscope.cn/datasets/facebook/tgve_plus.md"
repository: facebook/tgve_plus
last_updated: 2025-05-20
license: "Apache License 2.0"
storage_size: "119 KB"
downloads: 613
stars: 0
---

# tgve_plus

> tgve_plus - facebook 在 ModelScope 开源的数据集。Dataset Card for the TGVE+ Test Set

facebook/tgve_plus 是 ModelScope 魔搭社区上的数据集，存储大小 119 KB，采用 Apache License 2.0 许可。

- **Repository**: facebook/tgve_plus
- **License**: Apache License 2.0
- **Storage size**: 119 KB
- **Downloads**: 613
- **Stars**: 0
- **Last updated**: 2025-05-20

Source: https://www.modelscope.cn/datasets/facebook/tgve_plus

---

# Dataset Card for the TGVE+ Test Set

## Dataset Description

- **Homepage: https://fdd-video-edit.github.io/**
- **Paper: https://arxiv.org/abs/2403.09334**

### Dataset Summary

We extend the widely used Text Guided Video Editing (TGVE) benchmark with additional editing tasks. The dataset now comprises seven editing tasks in total: 
four from the original TGVE and three new tasks, namely (i) object removal (Remove), (ii) object addition (Add), and
(iii) texture alterations (Texture). The new tasks utilize the same 76 videos from the original TGVE benchmark.

Each row in the dataset consists of the instruction, input/output captions, and the relative path of the video in [TGVE](https://drive.google.com/file/d/1D7ZVm66IwlKhS6UINoDgFiFJp_mLIQ0W/view).
For more details please see our [paper](https://arxiv.org/abs/2403.09334) and [project page](https://fdd-video-edit.github.io/).

We'd like to thank [InstructVid2Vid](https://github.com/amazon-science/instruct-video-to-video) for creating instructions for the original TGVE tasks.


### Licensing Information

Licensed with CC-BY-NC 4.0 License available [here](https://creativecommons.org/licenses/by-nc/4.0/legalcode?fbclid=IwAR2SYZjLRywwUMblkWg0LyAxHVVTloIFlvC-ju3BthIYtOM2jpQHgbeXOsM).

### Citation Information
```
@inproceedings{Singer2024VideoEV,
  title={Video Editing via Factorized Diffusion Distillation},
  author={Uriel Singer and Amit Zohar and Yuval Kirstain and Shelly Sheynin and Adam Polyak and Devi Parikh and Yaniv Taigman},
  year={2024},
  url={https://api.semanticscholar.org/CorpusID:268385300}
}
```