| 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) |