---
license: apache-2.0
task_categories:
- image-to-image
---

# RORD-50 Dataset

This repository contains the **RORD-50** dataset, introduced in the paper [From Ideal to Real: Stable Video Object Removal under Imperfect Conditions](https://huggingface.co/papers/2603.09283).

[**Project Page**](https://xiaomi-research.github.io/svor/) | [**GitHub**](https://github.com/xiaomi-research/svor) | [**Paper**](https://huggingface.co/papers/2603.09283)

## Introduction
The RORD-50 dataset is a benchmark designed to evaluate video object removal performance under real-world challenges, such as shadows, abrupt motion, and defective masks. It was introduced as part of the **Stable Video Object Removal (SVOR)** framework, which focuses on achieving shadow-free, flicker-free, and mask-defect-tolerant removal.

## Overview
Removing objects from videos remains difficult in the presence of real-world imperfections. SVOR advances video object removal from ideal settings toward real-world applications by handling abrupt motion and mask defects effectively. This dataset provides the necessary benchmarks for testing the robustness and temporal stability of video inpainting models.

## Citation
If you find this dataset or the SVOR framework useful for your research, please consider citing the paper:

```bibtex
@article{hu2026svor,
   title={From Ideal to Real: Stable Video Object Removal under Imperfect Conditions},
   author={Hu, Jiagao and Chen, Yuxuan and Li, Fuhao and Wang, Zepeng and Wang, Fei and Daiguo, Zhou and Luan, Jian},
   journal={arXiv preprint arXiv:2603.09283},
   year={2026}
}
```

## Acknowledgement
This work benefits from the following open-source projects:
- [VideoX-Fun](https://github.com/aigc-apps/VideoX-Fun)
- [VACE](https://github.com/ali-vilab/VACE)
- [ROSE](https://github.com/Kunbyte-AI/ROSE)
- [SAM2 - Segment Anything Model 2](https://github.com/facebookresearch/sam2)
- [RORD](https://github.com/Forty-lock/RORD)