---
license: apache-2.0
task_categories:
- video-text-to-text
language:
- en
---
# VideoChat3-Academic2M

VideoChat3-Academic2M is the academic video instruction data used by VideoChat3. It re-annotates public academic video datasets for video captioning, video question answering, and fine-grained motion understanding.

The dataset follows an evidence-grounded annotation enhancement pipeline. Short answers, option-only labels, and concise captions are rewritten into richer instruction-following responses that mention visible objects, actions, scenes, temporal cues, and supporting evidence. A consistency filtering stage is then used to keep rewritten annotations aligned with the original academic labels.

This repository provides JSONL annotation files. The original videos are not duplicated here; users should resolve videos from the original dataset paths listed below.

[📄 Paper](http://arxiv.org/abs/2607.14935) · [🌐 Homepage](https://mcg-nju.github.io/VideoChat3/) · [💻 GitHub](https://github.com/MCG-NJU/VideoChat3) · [🤗 Paper Page](https://huggingface.co/papers/2607.14935)

<p align="center">
  <img src="academic2m.jpg" alt="VideoChat3-Academic2M Overview" width="100%">
</p>

## Data Sources

VideoChat3-Academic2M aggregates academic caption/QA sources including LLaVA-Video, Spoken-MIT, Vript, StarQA, Sports-QA, and Perception-Test.

| Source dataset | Original video dataset path |
| --- | --- |
| LLaVA-Video | https://huggingface.co/datasets/lmms-lab/LLaVA-Video-178K |
| S-MiT | https://arxiv.org/pdf/2105.04489 ([Re-uploaded by us](https://huggingface.co/datasets/MCG-NJU/VideoChat3-Academic2M/tree/main/S-MiT_videos)) |
| Vript | https://huggingface.co/datasets/Mutonix/Vript |
| StarQA  |https://github.com/csbobby/STAR_Benchmark |
| Sports-QA | https://huggingface.co/datasets/HopLeeTop/Sports-QA |
| Perception-Test | https://github.com/google-deepmind/perception_test |

The `acadmic2M.json` file provides the mapping between dataset annotations and video sources. You can refer to it when organizing the dataset structure.


## Citation

If you use this data, please cite VideoChat3 and the original video datasets used by the annotations.

```
@article{li2026videochat3,
  title={VideoChat3: Fully Open Video MLLM for Efficient and Generalist Video Understanding},
  author={Li, Xinhao and Zhu, Yuhan and Zeng, Xiangyu and Dong, Yuhao and Wu, Haoning and Zhang, Zhiqiu and Yang, Yuandong and Ma, Changlian and Zhang, Qingyu and Shi, Yansong and others},
  journal={arXiv preprint arXiv:2607.14935},
  year={2026}
}
```