---
license: apache-2.0
---

# OSCBench Dataset


### Dataset Description

OSCBench is a benchmark dataset designed to evaluate **object state change (OSC)** reasoning in text-to-video (T2V) generation models.

OSCBench organizes prompts into three scenario types:

- **Regular scenarios:** common action–object combinations frequently seen in training data.
- **Novel scenarios:** uncommon but physically plausible action–object pairs that test generalization.
- **Compositional scenarios:** prompts that combine multiple actions or conditions to test compositional reasoning.

### Dataset Statistics

The OSCBench dataset contains **1,120 prompts** organized into three scenario categories:

| Scenario Type | Number of Scenarios | Prompts per Scenario | Total Prompts |
|---------------|---------------------|----------------------|--------------|
| Regular       | 108                 | 8                    | 864          |
| Novel         | 20                  | 8                    | 160          |
| Compositional | 12                  | 8                    | 96           |
| **Total**     | **140**             | —                    | **1,120**    |


### Dataset Sources

- **Dataset:** https://huggingface.co/datasets/XianjingHan/OSCBench_Dataset  
- **Paper:** https://arxiv.org/abs/2603.11698  
- **Project Page:** https://hanxjing.github.io/OSCBench


## Acknowledgements and Citation

If you find this dataset helpful, please consider citing the original work:

```bash
@article{han2026oscbench,
  title={OSCBench: Benchmarking Object State Change in Text-to-Video Generation},
  author={Han, Xianjing and Zhu, Bin and Hu, Shiqi and Li, Franklin Mingzhe and Carrington, Patrick and Zimmermann, Roger and Chen, Jingjing},
  journal={arXiv preprint arXiv:2603.11698},
  year={2026}
}
```